How to get sufficient dev/random into the TPM like Security chips & mix it properly to secure confidentiality 2025 RS

Now in order to secure a good source of random for the Credit Card ASIC's, You surely need a good seed for the chip,

Availability of a tRNG on the card is unlikely & providing one may prove complex & expensive!

We can Seed a source DBRNG or cRNG, Fairly simply, Through a method we would call Doping, Sounding like you need to assure a unique ID..

Yes we need to secure a unique ID for the card & therefore we Dope the chip with a seed from a source such as:

sudo pollinate -r -i -s https://entropy.n-helix.com

or https://www.drand.love/get-started

Seeding the whole world daily could be a network intensive task,

However we could do it, monthly,

Or by transaction usage rate with repeated pollination per amount of transaction times..

We could seed from a reliable sourced unit or HSM on the net,..

I know for example that cards frequently source dev/random from the ATM.. Itself..

Maybe we can certainly require a bank to verify the machine with a unique TPM / HSM unit, Such as Yubikey's HSM by example..

We could still source a seed & this is logical for a TPM unit to do, To diversify the tRNG / cRNG pairing over time.

Rupert Summerskill

*

CC Card dev/random Seed, Secure Storage plans: RS

Simple Cards with ASIC's & TPM's should ideally keep around 16 * 1kb Seeds, Bearing in mind their size of chip,

A Credit Card is probably around 256KB of firmware at complex,

Bearing in mind that the card has NAND Flash with 30000 Writes or more..

So based on the fact that a CC Card is planned to last around 4 years?

16KB of total seeds stored from online data, Would be ideal, If they worked as a source of the DBRNG for around 1 month per 4 1kb Seeds,

In an ideal world, The Seed storage might be a total of 1MB with a 4KB Packing, So they could be threaded..

Presuming that the CC card uses a 32Bit or 64Bit CPU with SiMD Secure extensions.. & DBRNG to secure connections.

[1MB Seeds]
[4KB Packing]
[DBRNG, TPM]
[Network Firmware]
[Verification & PIN]

(c)RS

*

The Credit Card ASIC's only need 15KB of dev/random if they have a really good DBRNG,

The limitations of RSA AES are shown to be mildly insecure on a 512Bit Certificate!

TLS_ECDHE_MLDSA_WITH_AES_256_GCM_SHA384_P384 Requires around 4 Keys with lengths of 256Bit to 384Bit,

But they won't last forever, So the CC Card & our Networking needs an estimated dev/random seed production of around .. 4KB per minute,

4KB per minute provides reasonable security & Combining Seeds with DBRNG Magnification..

Provide what we need, On average AES takes 15 Minutes of Traffic to break, While watching a movie..

So a 4KB Seed tRNG combined with SHA + AES cRNG should work out mostly fine from the CC Card point of view.

Rupert Summerskill

*

We need some trust for transactions, I present to you: Environmental SRE with JIT..

TPM, Trusted Execution Environment (TEE) & Security Manager (TSM): Environmental SRE with JIT Compiler:

Setup protocols: requirements:

https://www.phoronix.com/news/Linux-6.19-PCIe-Link-Encrypt

TPM Development Files
https://filebin.net/5gz2eswycm9nl963/swtpm-0.10.0.zip

Ok so you are clearly expecting older hardware to support rDMA & Secure rDMA, While the technology of the Consumer GPU does not appear to support Secure VM extensions,

But does support Secure Media Playback, Through the 'Content Decryption Module' as present in Chrome & Firefox,..

For a start Media loading & texturing can be handled by the 'Content Decryption Module' & dcomp key exchange,

So our principles start with 3 essential requirements:

Compiled TPM Code, Strictly linear C# for conversion potentially to OpenCL & Shader code module..

C.D.M & Content Encryption Module, Basically TPM!,

But we have to compile it with advanced media extensions & Ethernet Protocols for rDMA..

Unless we can build the firmware with TPM & CEM code, We would still be loading the code from root drivers, Into the RAM,

Or from firmware into Device RAM,

Environmental SRE with JIT Compiler, Dongle code:

Side loaded with On device JIT Compiler with OpenCL Kernels,..

These are small Kernels that process our workloads..
They do not need to stay resident after completion,
But most tasks would require systematic resident & execute.

They work,
The principles described, Do not require too much from the device..

(c)RS

*

Certificate Train ProtoZakna with Cooky Cutter Store (c)RS

Machine Certificate, Sub Certificate creation with application certificates, Create an enclave of personal security...

Cooky Cutter & Secure Media Windows with DCOMP Hashes is one of my works,.. So! What is it?

Cooky Cutter is to use either JS Session Anonymous Client Certificate handling..

Or to use a now available WebAuthn &

Official Web Browser cooky containment & site isolated memory storage API

These 3 features when combined with Machine Certificate, Sub Certificate creation with application certificates, Create an enclave of personal security...

Combining these Temporary Certificate chains with TPM & HSM is very tempting!

However if you feel like we do, Security Cards like YubiKeys, TPM & HSM are tampered with.. Whisperers? What then!?

Firstly you can create a Machine Certificate & submit it to HSM then TPM then Security Cards.. 3 Certificates in a chain, Verified from source? Ok..

SHA Identifier, Does not reveal the certificate!:

So you have your train & if you trust nobody.. You verify the SHA Identifier against the 3 & Keep the certificate in the machine VM! Ok so you don't even trust this? So..

You verify the SHA of the Application with :  the chain : TPM, HSM, Card, Machine, Application, Application certificate store (with SHA verified against the machine).. Then!?

Verify the SHA? yes! of the Cooky Store encrypted memory! Against what? ANYTHING! lol

Verify the SHA versus the application store, Then Encrypt.. the secure hashes for windows with secure payments & personal information.. Involved!

You also Verify Cooky Cutter SHA verified AES Keys, To verify your interactions with the site & various functions..

Ok.. you are PARANOID, Probably don't trust anything! LOL, But it works!

(c) Rupert Summerskill

*

Chips, Cards - Dev/Random & Compute:

https://science.n-helix.com/2022/02/interrupt-entropy.html

https://science.n-helix.com/2022/06/jit-compiler.html

https://science.n-helix.com/2022/08/jit-dongle.html

*

https://filebin.net/5gz2eswycm9nl963/sufficient%20dev-random%20into%20the%20TPM%20like%20Security%20chips%202025%20%28c%29RS.txt

https://filebin.net/sog7knhxc5tuxbfe/LibKhaosECC-PQC%20dev-random%20RS.txt

https://filebin.net/5gz2eswycm9nl963/Dev-Random%20Random%20procedural%20drip%20driver.txt

https://filebin.net/sog7knhxc5tuxbfe/Client%20Security,%20Cooky%20identifier%20hashes%202024+RS.txt

https://filebin.net/5gz2eswycm9nl963/Cryptographic-Security-NIST.SP.800-57pt1r6.ipd.pdf

https://filebin.net/sog7knhxc5tuxbfe/Directory-Sort-RS.zip

TPM Development Files
https://filebin.net/5gz2eswycm9nl963/swtpm-0.10.0.zip

HSM-Yubi
https://filebin.net/sog7knhxc5tuxbfe/YubiHSM-deb.zip

Security Card practicality:

https://www.usmartcards.com/Types-of-Smart-Cards

https://filebin.net/5gz2eswycm9nl963/PQC-Smartcard-NIST2025.pdf

https://filebin.net/5gz2eswycm9nl963/Access-Tokens+Privacy+Security-NIST.IR.8587.ipd.pdf

https://filebin.net/5gz2eswycm9nl963/Cryptographic-Security-NIST.SP.800-57pt1r6.ipd.pdf

*

Average Data Fingerprinting removal, Using multisource tRNG blending: RS

Perhaps using 2 seeds, One for Ubuntu Pollinate & One from DRAND, Makes sense if you wanted to hash it together..

What you would do is call both, But you could call the device tRNG, If firmware based on ASIC..

All you are doing is cleaning up the Average Data Fingerprinting..

In the Drip driver, We salt the dev/random with 4 sources like so:

beacon1 = fetch_verified_drand()
beacon2 = fetch_verified_pollinate()
trng = read_device_trng(32)
os32 = os.urandom(32)

If we use in addition of DRAND, Pollinate & Firmware Seed, & If we have the power, A tRNG too!
We can even use a cRNG pair that load a fresh seed, That is stored for a number of uses..
So we don't over exploit the beacons.

It is that simple!

RS

Choose primitives:

HKDF‑SHA256, DRBG‑CTR‑AES256 or HMAC‑DRBG, optional SHAKE‑128 whitening..

DRBG-CTR-AES256 Deterministic random bit generator with..
SHAKE-128, Arbitrary length hash function for hashing,

Thereby obscuring total length of submitted dev/random seed

# DRBG-CTR-AES256, a new deterministic random bit generator, has been added..
# SHAKE-128, an arbitrary length hash function of the SHA-3 family, has been added

#Seed Provision : RS https://www.drand.love/get-started
# A good smooth start for a very code friendly solution : RS

# Drip driver v1 (Python)
import os, hmac, hashlib, time, secrets
from hashlib import sha256

def hkdf_extract(salt: bytes, ikm: bytes) -> bytes:
return hmac.new(salt, ikm, sha256).digest()

def hkdf_expand(prk: bytes, info: bytes, L: int) -> bytes:
t = b""; okm = b""; i = 1
while len(okm) < L:
t = hmac.new(prk, t + info + bytes(), sha256).digest()
okm += t; i += 1
return okm[:L]

def derive_material(beacon: bytes, trng: bytes, os32: bytes, run_id: bytes, secret_salt: bytes):
prk = hkdf_extract(secret_salt, beacon + trng + os32 + run_id)
okm = hkdf_expand(prk, info=b"drip-driver-v1", L=64)
whiten_key = okm[:32]; master_key = okm[32:]
whitened = hmac.new(whiten_key, okm, sha256).digest()
return {"master_key": master_key, "whiten_key": whiten_key, "whitened": whitened}

# Example integration points (stubs to replace with real implementations)
def fetch_verified_drand() -> bytes:
# Return beacon value bytes; include round ID separately in logs for audit
raise NotImplementedError

# I suggest fuzzy multi source dev/random Myself RS
def read_device_trng(n=32) -> bytes:
# TPM/TRNG/KhaosKey; fall back to os.urandom if unavailable
return os.urandom(n)

# Drip loop
def drip_round(secret_salt: bytes, run_id: bytes):
beacon = fetch_verified_drand()
trng = read_device_trng(32)
os32 = os.urandom(32)
material = derive_material(beacon, trng, os32, run_id, secret_salt)
return material # store audit_tag/run_id, never log keys

def jittered_sleep(base_ms=250, jitter_ms=200):
# Ordered I/O with mild disorder: bounded jitter
delay = base_ms + secrets.randbelow(jitter_ms+1)
time.sleep(delay / 1000.0)

#(c)RS

# A suggested whitening pattern

# SplitMix64 -> FP in [0,1)
def splitmix64(x: int) -> int:
z = (x + 0x9E3779B97F4A7C15) & ((1<<64)-1)
z = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9 & ((1<<64)-1)
z = (z ^ (z >> 27)) * 0x94D049BB133111EB & ((1<<64)-1)
return z ^ (z >> 31)

def to_uniform01(u64: int) -> float:
return (u64 >> 11) * (1.0 / (1<<53))

#RS

*

#Seed Provision : RS https://www.drand.love/get-started
# A good smooth start for a very code friendly solution : RS

#Python

#V2 Beacon-Salt-Driver

import os, hmac, hashlib, time, secrets
from hashlib import sha256

def hkdf_extract(salt: bytes, ikm: bytes) -> bytes:
return hmac.new(salt, ikm, sha256).digest()

def hkdf_expand(prk: bytes, info: bytes, L: int) -> bytes:
t = b""; okm = b""; i = 1
while len(okm) < L:
t = hmac.new(prk, t + info + bytes(), sha256).digest()
okm += t; i += 1
return okm[:L]

def verify_beacon(beacon_bytes: bytes, signature: bytes, pubkey) -> bool:
# Placeholder: verify beacon signature using stored pubkey
raise NotImplementedError

def trng_health_check(data: bytes) -> bool:
# Lightweight checks: no long runs of identical bytes, basic entropy heuristics
return True

def derive_material(beacon: bytes, trng: bytes, os32: bytes, run_id: bytes, device_salt: bytes):
ikm = beacon + trng + os32 + run_id
prk = hkdf_extract(device_salt, ikm)
okm = hkdf_expand(prk, info=b"card-seed-v1", L=64)
return {"master_key": okm[:32], "whiten_key": okm[32:64]}

def drip_round(device_salt: bytes, run_id: bytes, beacon, beacon_sig, beacon_pubkey):
if not verify_beacon(beacon, beacon_sig, beacon_pubkey):
raise RuntimeError("Beacon verification failed")
trng = os.urandom(32)
if not trng_health_check(trng):
raise RuntimeError("TRNG health check failed")
os32 = os.urandom(32)
material = derive_material(beacon, trng, os32, run_id, device_salt)
# Store master_key encrypted in NV with AEAD; store run_id and monotonic counter
return material

# A suggested whitening pattern

# SplitMix64 -> FP in [0,1)
def splitmix64(x: int) -> int:
z = (x + 0x9E3779B97F4A7C15) & ((1<<64)-1)
z = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9 & ((1<<64)-1)
z = (z ^ (z >> 27)) * 0x94D049BB133111EB & ((1<<64)-1)
return z ^ (z >> 31)

def to_uniform01(u64: int) -> float:
return (u64 >> 11) * (1.0 / (1<<53))

#(c)RS

*

#Seed Provision : RS https://www.drand.love/get-started
# A good smooth start for a very code friendly solution : RS

#Python V3

# beacon_seed.py (concept)
import requests, hmac, hashlib, os, base64
from cryptography.hazmat.primitives import serialization
# BLS verification requires a library that supports BLS signatures

DRAND_URL = "https://api.drand.sh/public/latest"
Polinate_URL = "https://entropy2.n-helix.com"

def fetch_verified_drand():
r = requests.get(Polinate_URL, DRAND_URL, timeout=5)
r.raise_for_status()
j = r.json()
# j contains 'round', 'randomness' (base64), 'signature'
randomness = base64.b64decode(j['randomness'])
round_id = j['round']
signature = base64.b64decode(j['signature'])
# Verify signature with stored drand public key (implementation required)
# TODO: verify signature using stored drand public key and BLS verify
# Example: bls_verify(drand_pubkey, randomness, signature)
return randomness, round_id, signature

def hkdf_extract(salt: bytes, ikm: bytes) -> bytes:
return hmac.new(salt, ikm, hashlib.sha256).digest()

def hkdf_expand(prk: bytes, info: bytes, L: int) -> bytes:
t = b''; okm = b''; i = 1
while len(okm) < L:
t = hmac.new(prk, t + info + bytes(), hashlib.sha256).digest()
okm += t; i += 1
return okm[:L]

def derive_seed(beacon, trng, os32, device_salt, run_id=b'01'):
ikm = beacon + trng + os32 + run_id
prk = hkdf_extract(device_salt, ikm)
okm = hkdf_expand(prk, b"card-seed-v1", 64)
return okm[:48] # seed material

#(c)RS

*****

Card TPM AHEAD FS:

The requested Seeds can be stored in RAM, Based on the Infineon TPM, I am expecting around 24kb,

This is far too little for 4KB fetches & operations in ECC, However I will extrapolate a plan for such a value & plan..

AHEAD FS:

Formally if we have seeds stored, We will use them, Including Battery Backed RAM..

We will pre compute the server certificate & request seeds.. A single fetch set ..

We will be fetching a multiple of 4 seeds of 256bit each for our operation..

We will, DRBG, Hash, Blend, XOR etcetra to keep the seed formula a secret..

Firstly I will base the value of the Certificate at 384/256Bit & load it into ram from storage..

No, We will directly rDMA the certificate to a direct CPU / Secure Crypto Instruction for ECC + AES,

If we have RAM space available to plan the full Certificate & Pin process, With verification..

This will save loading & emptying our cache.. speeding up operations..

We will verify the cards authenticity & pin in encrypted message hash, With a Seeded DRBG-CTR-AES256..

We will validate the amount of the bill by approximation, Authourise &..

With a validated payment amount, requesting the funds..

During this cycle we shall also verify the service is valid & that the bill is tabulated..

We will formalise the session & cache seeds in our Battery Backed RAM.

Rupert S

*

24KB ROM Efficiencies, Network cards TPM & Secure Cards: RS

I appreciate that a TPM or Security or Credit Card may have 24KB of RAM, Most Python & JS DRBG code would have to be re-compiled into Efficient ASM,

Strategies for 24KB RAM:

Most devices have Firmware compression, Linux & Windows Firmware for example:

Execution Runtime Compression with Caching & load:

Code compression, Firstly LZH, GZIP, Brotli, Efficient code compression would not slow down a card, Stored in ram,

Code compression would not really slow down a 33Mhz ARM NANO, Freeing RAM & Storage,..

GZIP & LZH are efficiently used on the AMIGA & ATARI & on ROM Systems such as NES & SAGA, PlayStation 1+

Optimise code for compression & Verification Certification..

Code optimisations:

Faster code:

Use identical code with multiple encryption systems & shared libraries with efficient code base pruning..

Unrolling Loops for faster code: With 24KB RAM
We would also have to think about loops in code, Because loops would be more RAM Efficient..

rDMA, The newer DMA systems include cache transfer, security & Low latency IO..

Load & Unload with segment referencing, Allow less frequent code runtimes to be directly run, through cache .. On NAND Flash, With segment caching & loop caching, Ordered treading..

Latency, Reduce calls to direct call order & optimise the pre-loading with cache..

Remember to compress everything that you can, Execution Runtime Compression with loop caching in processor cache..

Rupert Summerskill

*

Credit Card working ram said to be 1KB! So plans will have to be made! RS

Credit Card Component Typical Size
Secure storage (EEPROM/Flash) 4 KB – 128 KB
Working memory (not RAM in the usual sense) ~1 KB or less
Magstripe data

1KB means that the atm machine will have to do the work, 128KB of flash means the storage of ML-KEM & Cryptographic seeds is possible...

But from my angle, most of the RAM will have to be repurposed as cache..

Cache & PIN, Not cash & pin! Now how do we handle the low RAM issue & still do everything with the card?

Point of view 1:

The entire ML-KEM Certificate stored in Card RAM? Seems impractical.. So will be cache..

FLASH stored MK-KEM, Certificate, With multiple calls to RAM for flash? Including dev/random SEEDS

Secure Enclave on the ATM / Card Payment machine ... Using SoftTPM?

rDMA Certificate identifier transferred to machine ?

Ideal card would contain enough RAM, To store 4 seeds & ML-KEM Certificate & AES + Crypto Functions..

Point of view 1b : +1

Security of the ATM, Excludes sending the certificate to the machine, ..

We would have to send the client pem from the stored certificate & verify each verification cycle to the card..

In effect, We would have to use features of the card, Having security features like AES & Crypto..

ATM : Send, Receive from bank,

rDMA to card & cyclically receive a client verifier to hash to the server..

Security operations, All carried out by the card!

RS

The situation is not ideal, The ideal situation is like a YubiKey that shall process all secure Crypto functions on the card,..

I am assuming that the card shall be equipped with firmware that enables base maths for ML-KEM & AES & SHA2/3..

I am assuming that the ideal ram is around 256KB, But for the price of cards, Maybe 64KB..

I am assuming that, Network operations, Using the ATM Buss, With the cards rDMA, Is possible!

(c) Rupert Summerskill

*

Crypto : Lower memory operation strategy : RS

On a TPM / Security card with 33Mhz ARM with Secure Vector extensions, MLKEM would be entirely possible..

The MLKEM's would have to use Vector Matrix Extensions, Lattices in other words...

ML-kEM PQC

Public Key Size (bytes)Private Key Size (bytes)Cipher Text Size (bytes)
Kyber512 800, 1632, 768
Kyber768 1184, 2400, 1088
Kyber1024 1568, 3168, 1568

Public key size (bytes)Signature size (bytes)
Dilithium 2 1312, 2420
Dilithium 3 1952, 3293
Dilithium 5 2592, 4595

Now as you can see, 24KB RAM Limit is going to cause significant issues.. To loading & operating KEMS..

We are going to have to directly operate the certificate from NAND Flash & hope this is fast enough,

To speed up the process, We are going to have to use partial buffering cache, Because a Nano ARM CPU Cache is undoubtedly too small,

Bear in mind that an AMD Epyc has 64KB of L1 Cache & maybe 512KB of L2 & 8MB L3...

An ARM CPU may have enough Cache on die, To activate MLKEM's from cache,..
Bear in mind that all a TPM or Security Card or HSM does is security ..

The system may work as follows:

CPU + L1, L2, L3 Cache runtime,

Pull from on board Static Buffer... 256KB to 4KB

Repeatedly run Cryptographic operations.. From Buffer..

Load & unload, As needed!

Including a configuration such as..

TLS_ECDHE_MLDSA_WITH_AES_256_GCM_SHA384_P384

May therefore be entirely possible, On lower configurations such as : Kyber768 & Kyber512

Rupert S

*

DML & Lattices: RS

As stated about ML-KEM, Lattice maths strategy from my point of view, Involves DML, DirectX ML Certification..

[ ], [ ], How does this work? GPU, NPU, AVX Lattice maths for DML..

With SiMD & AVX, You have to use parallel line pipes, Like so:

A lattice array can be seen as a parallel, Line by line maths objective:
Fortunately, AVX 256 & AVX 512 can parallelise, Integers & floats in 8Bit, 16Bit, 32Bit, 64Bit:
So work lanes can be used, As there is no cross lane relational maths from advanced lattices:

[ ], [ ], [ ], [ ],
[ ], [ ], [ ], [ ],
[ ], [ ], [ ], [ ],
[ ], [ ], [ ], [ ],

So we can handle lattices with SiMD!
We also have solver units such as NPU & Lattice accelerators such as found in ARM SVE,

Credit Cards & Security Cards & TPM/HSM with ARM & X86, Could handle ML-KEM's..

We could also use DML Certified hardware! But at least look at the operations available in the Windows DML Certifications for machine learning.

Rupert Summerskill

*****

Entropy coefficients (c)RS

Whitening is code that, Makes noise sound more average, For a long time I have been a promoter of 'Chaos as a system', ..

Entropy is the average gaussian noise like at the end of the universe, Where a small amount of matter appears & disappears..

Entropic Average Noise.. is noise with a deviation from gaussian that is more random..

The problem for me, Is that in maths .. average gaussian noise reduction, Is where the majority of the noise is evenly spaced (On average)..

The majority of Entropy Noise is Gaussian in nature, greater average deviations from gaussian noise are mostly removed..

What remains is bearly differentiated from gaussian noise,

Positive points of Lossy Gaussian Average Noise, AKA Entropy:

Subtle differences hide the encryption details,..
One argument for this process is that deviations from average.. Are subtle!

Average Deviation with Higher Error Rate Gaussian.. Principly is maths provable .. To flip a coin,

Subtle differences from true 50/50, Make Entropy harder to read..

Liber Khaos, Clever modifications on entropy, That make it chaos:

Larger noise variance, Does not involve heavy whitening, It involves a very low modification of the state of entropy .. over time or area..

The way this works is to deviate the noise a tiny bit,..

Lattice Maths & Entropy or Subtly Whitened Entropy:

To subtly whiten, means to modify the values of the sampled noise .. by a small amount in various ways..

Subtle Elliptic Curves

Rules that,..

Roll,

Add & Subtract or multiply specific results..

To vary by percentage & rules that shift values..

Up or Down based on placement in a lattice..

These, Add & Subtract or multiply specific results in a lattice,

The size of the lattice matters, sizes of 2D: 5x5, 8x8, 16x16 Lattices are recommended

Sizes of 3D Lattice: Nx5x5, Nx8x8, Nx16x16, Suggested values for N = 1, 2, 3, 4, 5

Rupert Summerskill

*

We have some hope of using a QRNG, My python has QRNG, But they are maths based! RS

https://www.wolfssl.com/direct-entropy-injection-in-ml-kem-understanding-makekeywithrandom-and-drbg-bypass/

We can hope to use a QRNG for many functions & surely we could secure Credit Cards, TPM, HSM with Software QRNG's,

Study of cards ROM space indicates that ram could be as low as 24KB! Flash storage in the range of 1MB, We could do it!

Quantum Rings in terms of software, Rely on emulation of quantum effects, Fortunately they rely on a very small part of the SDK's..

We could though & Wolf SSL is ideal, Having used Wolf SSL on my patched router .. Instead of OpenSSL, The functions are promising!

You have to consider that WolfSSL is a fully secure Compiled SDK & provides FIPs Security..

Think about QRNG in terms of software, We obviously could not upgrade a current generation of cards to QRNG, But Soft QRNG's we could manage!

The conceptual space of post quantum QRNG's & Wide sized Quantum based ECC, Could considerably enhance our future security..

We just have to replace the cards & in the meantime, Replace the firmwares.

Rupert Summerskill on behalf of world security.

*

The Quantum TNG! & Software Emulations, Through Python IBM Kit: (c)RS

Software QRNG are relying on a point of view of Quantum events, That is Quantum Machines work on ripples & boundaries, What quantum events rely upon, Is a variation of probability over Area or Energy ripples..

The effect of quantum computers is mostly based around probabilities, Large Maths with higher precision can represent these features, ..

With thousands of FPU per GPU, Relatively simple to represent!

However, this being said, Quantum Computers are much more precise! How much more precise? THOUSANDS of % more precise!

Dealing with the effects of quantum computers is difficult! However if we wish to present causal reality at much less resolution, Say the FPU 183Bit or Double Float, Probabilities?

We could, Represent this!

How does this affect us? Well being practical QRNG can be said to channel through the CPU/GPU/FPU & for a start, We need to present our GRAND dev/random, The Quantum TNG!

So we can do two proposals:

Channel True QRNG's

Channel Emulated Post Quantum software version, Such as present in my Python installs..

IBM Quantum Emulation : QKit, May take a lot of resources, However small things like QcRNG, QtRNG & All emulation tasks ?

Entirely potentiated!

(c)Rupert Summerskill

*****

"C:\Program files\go\bin\go" install github.com/drand/drand@latest

"C:\Program files\go\bin\go" install github.com/drand/go-clients@latest

drand generate-keypair

drand generate-keypair localhost:500

drand generate-keypair --id {} localhost:500

drand start

drand sync

drand share

drand start --public-listen 127.0.0.1:500 --control 8881

*****

*RAND OP Ubuntu : https://manpages.ubuntu.com/manpages/trusty/man1/pollinate.1.html

(copy this text, for your server) *pollinate* /dev/rnd every 3 hours if you like:

sudo pollinate -r -i -s https://pollinate2.ns4v.icu
sudo pollinate -r -i -s https://entropy.n-helix.com
sudo pollinate -r -i -s https://entropy2.n-helix.com

https://www.drand.love/get-started

Pollinate nodes : https://pollinate.n-helix.com/ https://pollinate2.n-helix.com/ https://pollinate2.ns4v.icu

https://science.n-helix.com/2018/12/rng.html

https://science.n-helix.com/2022/02/rdseed.html

https://science.n-helix.com/2017/04/rng-and-random-web.html

https://science.n-helix.com/2022/02/interrupt-entropy.html

https://science.n-helix.com/2018/05/matrix-of-density.html

https://science.n-helix.com/2019/10/classic-physics.html

https://science.n-helix.com/2021/11/monticarlo-workload-selector.html

https://science.n-helix.com/2022/03/security-aspect-leaf-hash-identifiers.html

https://science.n-helix.com/2022/02/visual-acuity-of-eye-replacements.html

https://science.n-helix.com/2022/06/jit-compiler.html

https://science.n-helix.com/2022/08/jit-dongle.html

*****

references:

https://filebin.net/sog7knhxc5tuxbfe/Directory-Sort-RS.zip

Chaos
https://science.n-helix.com/2022/02/interrupt-entropy.html
https://science.n-helix.com/2022/02/rdseed.html
https://science.n-helix.com/2020/06/cryptoseed.html

https://science.n-helix.com/2018/12/rng.html
https://science.n-helix.com/2017/04/rng-and-random-web.html

https://csrc.nist.gov/projects/interoperable-randomness-beacons

https://www.drand.love/

https://www.drand.love/get-started

https://www.randa.mu/

https://github.com/drand/drand

PQC:

https://blog.cloudflare.com/pq-2025/

https://blog.cloudflare.com/bootstrap-mtc/

TPM Development Files
https://filebin.net/5gz2eswycm9nl963/swtpm-0.10.0.zip

HSM-Yubi
https://filebin.net/sog7knhxc5tuxbfe/YubiHSM-deb.zip

Security Card practicality:

https://www.usmartcards.com/Types-of-Smart-Cards

https://filebin.net/5gz2eswycm9nl963/PQC-Smartcard-NIST2025.pdf

https://filebin.net/5gz2eswycm9nl963/Access-Tokens+Privacy+Security-NIST.IR.8587.ipd.pdf

https://filebin.net/5gz2eswycm9nl963/Cryptographic-Security-NIST.SP.800-57pt1r6.ipd.pdf

*****

24KB, 33mhz, Compression: (c)RS

With 24 KB RAM you must treat firmware as a streaming, segmented runtime:

compress code on flash, decompress only hot segments into RAM, implement tiny verified crypto kernels in hand‑tuned assembly, and batch seed writes to protect NAND endurance..

This approach yields workable DRBG/reseed cycles on constrained TPM‑like cards while keeping auditability and health checks intact.

Compact strategy summary

Compression first: Store firmware and large tables compressed; use a tiny decompressor to inflate only required segments at runtime..

Fixed‑size decompressor RAM can be as small as ~2 KB for many codecs.

Segmented kernels: Keep one compact AES/HKDF/ECC kernel in RAM; load other primitives on demand from compressed flash..

Design decompression to target 512–2 KB working sets.

ASM crypto: Reimplement DRBG and whitening in optimized assembly with careful register allocation and minimal stack spills,..
Prefer loop reuse over aggressive unrolling to save RAM.

DMA + cache streaming: Treat NAND as segmented cache: rDMA fetches compressed segments into a small cache window, then decompress and execute.

Batch seed writes: Accumulate reseed events and commit in bursts with wear‑leveling journaling to minimize NAND writes..

Lightweight compression (LZSS/heatshrink) can run in very small memory footprints..


Option RAM footprint Best use
DEFLATE/GZIP ~2 KB decompression buffer typical Good ratio for code/data; slightly larger decoder
LZSS / heatshrink <1 KB possible Very small RAM; ideal for microcontrollers
Custom LZ variant tunable RAM vs speed Best when you control compressor on host

DMA + decompression scheduler strategy:

while(true):
if seed_needed:
dma_fetch(compressed_segment)
verify_mac(segment)
decompress_block_into(window)
execute_kernel_using(window)
process_transactions()
if batch_commit_needed: write_journaled_seeds()

RS

Memory map:

Layout (example for 24 KB RAM)
Resident decompressor 2 KB — decompressor + small working buffers.

Crypto kernel 6 KB — AES/HKDF/ECDH minimal ASM routines and tables.

DRBG state 128 B — state, counters, health flags.

Seed buffer 4 KB — incoming compressed seed blocks / staging.

Scratch / stack 4 KB — temporary workspace for decompression and ECC ops.

DMA cache window 6 KB — compressed blocks fetched from NAND.

Decompressor strategy:

Choose tiny LZ variant: LZSS/heatshrink style decompressors can run in <1–2 KB RAM and are proven for microcontrollers.

Segmented blobs: store functions/tables as independently signed compressed segments; verify MAC/signature before decompression.

Streaming blocks: decompress only the block containing the hot loop or table to keep peak RAM bounded..

ASM crypto kernels:

AES/HKDF core in ASM: use a single shared AES core (CTR/GCM) and HKDF implemented over HMAC-SHA256; optimized Cortex‑M AES implementations exist and show large speed/size gains when hand‑tuned.

DRBG state minimalism: keep state <128 B; call reseed only after health checks and when usage thresholds hit.

Implementation checklist (practical, prioritized):

Implement a 1–2 KB decompressor stub and host‑side compressor pipeline to produce segmented blobs.

Port DRBG to ASM with minimal stack use; keep HKDF/AES core as single shared routine.

Design DMA fetch + decompress scheduler to prefetch kernels before transaction bursts.

Add lightweight health tests (repetition/adaptive proportion) before accepting local entropy.

Batch and journal seed writes with anti‑rollback counters and MACs to protect integrity and NAND life.

[Measure and tune]: profile decompression latency vs transaction deadlines and adjust segment sizes.

Important: choose a decompressor whose RAM working set you can bound at build time,..

emCompress‑style approaches let you trade ROM for tiny fixed RAM budgets.

Risks & mitigations:

Decompression DoS — limit decompressor CPU budget and verify signatures before inflating signed blobs.

NAND wear — batch writes, rotate pages, and compress seeds before storage.

Timing leaks — constant‑time crypto kernels and prefetch scheduling.

Rupert S

*

Compression PT2: (c)RS

For TPMs, secure elements, and smartcards with ~24 KB RAM, treat compression, code layout, DMA, and tight assembly as first‑class design constraints.;

1. Pick compression to match decompressor budget

Goal: minimize ROM footprint while keeping decompressor < a few KB of code,

LZSS/LZ variants are the usual sweet spot for tiny secure devices because their decompressor can be implemented in a few hundred bytes to a few KB of C/ASM and still give good compression on repetitive code and tables..

Tools and community implementations (including game/ROM toolchains) show practical, tiny LZSS implementations.

UEFI/firmware precedent: mainstream firmware uses compressed images extracted into memory at boot; the spec emphasizes small, fast decompressors for boot‑time performance — the same tradeoffs apply to secure elements where RAM is the scarce resource.

2. Execution model: segmented compressed firmware with a tiny loader

Segment framing: store firmware as independently compressed segments (functions, crypto primitives, tables)..

Each segment has a header with size, checksum/MAC, and a small integrity signature,..

Decompress only the segment you need into a fixed execution window (e.g., 4–8 KB).

Hot vs cold code: keep hot crypto loops and small shared primitives resident; swap cold management code in/out on demand..

This is effectively manual virtual memory for microcontrollers and smartcards.

Integrity: verify each segment before execution (MAC or signature) to meet security/certification requirements.

3. Decompressor design and placement

Tiny decompressor in ROM/flash: implement the decompressor in compact ASM/C so it fits in the resident window..

Prefer streaming decompression (block by block) to avoid needing the whole decompressed segment in RAM at once.

Cache the decompressor state: if the decompressor is used frequently, keep it resident and reuse buffers to avoid repeated initialization overhead.

Block framing: design compressed segments with small blocks so you can decompress only the block containing the hot loop or table you need.

4. Code layout and compression‑friendly coding

Make code compressible: identical code paths, table‑driven logic, and avoiding unnecessary inlining improve compression ratios dramatically..

Shared helper routines compress better than duplicated code.

Binary layout: place similar functions and data close together (e.g., all crypto primitives in one region) so LZ matches find long repeated sequences.

Data vs code: separate large constant tables (S‑boxes, round constants) into their own compressible segments so they can be cached or streamed independently.

5. Crypto primitives and DRBGs in ASM
ASM for size and timing: hand‑tuned assembly reduces code size, gives deterministic timing (important for side‑channel resistance), and allows precise register allocation to avoid stack/heap usage..

Implementing AES/SHA/etc, As shared primitives used by DRBG and higher protocols.

DRBG state minimization: keep DRBG state tiny (dozens of bytes), avoid dynamic allocation, and use streaming entropy input where possible. Reuse the same AES/SHA primitives to avoid code duplication.

Partial unrolling: unroll only inner rounds where it measurably reduces branches or timing variance; otherwise prefer loops to save ROM and improve compression.

6. Loop unrolling tradeoffs

When to loop: loops are far more ROM‑efficient and compress better because repeated instruction sequences compress well..

For 24 KB RAM devices, favor loops unless unrolling is required for timing or to eliminate branches that cause side‑channel leakage.

When to unroll: inner cryptographic rounds where branchless, constant‑time code is required and where the CPU cost of branches outweighs the ROM penalty.

Use partial unrolling to balance size vs speed.

7. DMA, rDMA, and cache strategies

Use secure DMA: stream large data (entropy pools, large buffers) from flash to RAM via rDMA to avoid copying through CPU and to reduce resident RAM needs,

Ensure DMA respects memory region protections.

Loop caching and prefetch: design hot loops to fit in the processor’s instruction cache or the execution window so they execute in place after being decompressed once..

Prefetch tables into a small RAM scratch area before heavy use.

Ordered threading: schedule decompression and DMA transfers to overlap with CPU work where possible (e.g., decompress next segment while CPU processes current data).

8. Latency and call graph optimization

Flatten hot paths: reduce call depth and indirect calls on hot paths to minimize stack usage and call overhead..

Tail‑call elimination and inlining of tiny helpers (when it doesn’t bloat code) help.

Static allocation: avoid heaps and dynamic stacks; use fixed scratch buffers and a small, well‑bounded stack to simplify verification and reduce runtime surprises.

9. Verification and certification considerations

Deterministic decompression: use simple, auditable decompression code to ease formal review and certification. Complex decompressors (large state machines) increase audit surface.

Segment integrity: sign or MAC each compressed segment so the loader can verify authenticity before execution — this aligns with secure‑boot and TPM attestation models.

Auditability: prefer simple, well‑documented algorithms (LZ variants, DEFLATE) with small reference implementations to simplify certification evidence.


10. Practical checklist to implement

Choose compressor: start with an LZSS/LZ variant for best decompressor footprint.

Design segments: split firmware into hot/cold segments with headers and MACs.

Write tiny loader: implement streaming decompression in ASM/C and keep it resident.

Implement shared crypto primitives: AES/SHA in constant‑time ASM, used by DRBG.

Minimize DRBG state: keep state <128 bytes; reuse primitives.

Use rDMA for bulk transfers: stream large tables and buffers.

Layout for compression: colocate similar code/data to improve match lengths.

Test performance: measure decompression latency on target CPU (e.g., 33 MHz) and tune block sizes.

Prepare certification artifacts: provide simple decompressor source, segment signing scheme, and memory usage proofs.

Rupert S

*****

TPM, Trusted Execution Environment (TEE) & Security Manager (TSM): Environmental SRE with JIT Compiler:

How the key components and how they fit together..

TPM / software TPM for development — Use a TPM implementation during development and CI to exercise attestation, key provisioning, and sealed storage before moving to hardware TPMs or firmware‑embedded chips.

PCIe link encryption and device authentication — Kernel and platform support for PCIe link encryption (Linux 6.19 work) is a foundational layer to protect device ↔ host traffic and to enable Trusted I/O models for devices that support it.

rDMA / RoCE surface — RDMA and RoCE introduce low‑latency paths that bypass host stacks,..

They require explicit protections (isolation, authentication, integrity), Because RNICs and in‑network attackers can otherwise subvert confidentiality and integrity.

Content Decryption Module (CDM) / Encrypted Media Extensions — Consumer GPUs expose secure media playback primitives via CDMs/EME,..

These can be repurposed as a constrained secure execution and key‑exchange channel for media/texturing workloads..

Where full TEE/VM extensions are absent, Or you need more protection ability..

Architecture proposal:

High level flow

Boot / provisioning: device firmware or root driver performs TPM attestation and fetches signed policy and kernel blobs..

Use swtpm in dev cycles to validate flows & as an advantaged addition to sourcery!

Secure channel establishment: host and device negotiate link security (PCIe link encryption when available) and authenticate device identity before any sensitive payloads are transferred.

Keying and CDM handshake: use CDM/EME style key exchange for media/texturing secrets when GPU lacks full TEE/VM support; CDM acts as a constrained TEE for media pipelines.

On‑device JIT kernels: small OpenCL/shader kernels are signed and attested; they are loaded into device RAM or executed via the CDM‑protected path..

Kernels are ephemeral by default but can be resident if policy allows.

rDMA usage: if using rDMA for bulk transfers, enforce RNIC isolation, authenticated queue pairs, and fabric isolation to prevent unauthorized memory access.

Threat model and mitigations:

Host compromise — If host is compromised, signed firmware and TPM attestation prevent silent substitution of kernels or keys; prefer firmware‑level anchors for highest assurance..

However Verified Kernel loading in Shaders, OpenCL & Direct Compute.. Is logical & offers an unrestrained load/run cycle compared to limited use firmware with low storage limits.. RS

In‑network or RNIC attacks — RDMA bypasses host stacks; require authenticated queue pairs, fabric isolation, and RNIC firmware validation to prevent unauthorized memory access.

Device firmware tampering — Enforce secure boot, signed firmware images, and measured boot with TPM PCRs; validate device identity before provisioning secrets.

Side‑loaded JIT risks — Limit JIT kernel capabilities (no arbitrary DMA), require kernel signing, and use attestation to bind kernels to a session and to a CDM/TEE instance.

Practical integration steps and engineering checklist:

Dev environment

Stand up swtpm for CI and local testing of attestation and key lifecycle.

Kernel and platform

Track and enable PCIe link encryption/device authentication support on target kernels and platforms; test with hardware that supports SEV‑TIO or equivalent when available.

rDMA hardening

Implement RNIC firmware validation, authenticated QP setup, and fabric isolation tests; run adversarial RDMA test suites to validate protections.

CDM integration

Prototype key exchange and secure media/texturing flows using an open CDM implementation or EME APIs to validate constrained TEE behavior for GPU workloads.

JIT kernel lifecycle

Define signing, attestation, and expiry policies for kernels; implement ephemeral keying and rotate kernels frequently. Enforce capability restrictions in the kernel loader.

Operational controls

Logging, remote attestation audits, and revocation lists for compromised devices or kernels. Automate firmware signing and secure update pipelines.

#CEM-Next-Experiments:

Proof of concept: run a pipeline where a signed OpenCL kernel is attested via swtpm, delivered over an encrypted PCIe link (or simulated), and executed inside a CDM‑protected media path..

Use ephemeral kernels and measure attack surface..

Start with swtpm for TPM flows and a Linux 6.19+ kernel tree to exercise PCIe link encryption hooks.

RDMA adversary tests: run ReDMArk‑style tests against RNICs and RoCE setups to discover practical gaps and harden the fabric before production use.

Policy hardening: codify kernel signing, attestation checks, and CDM constraints into a security manager (TSM) that enforces runtime policies.

CEM Secure Protocol priorities:

Priority 1: anchor trust in a hardware or firmware root (TPM + secure boot) and validate with swtpm in dev cycles.

Priority 2: protect device ↔ host transport (PCIe link encryption) and treat RDMA as a high‑risk path requiring explicit isolation and authentication.

Priority 3: leverage CDM/EME primitives for constrained secure execution on consumer GPUs where full TEE/VM extensions are unavailable; use signed, attested, ephemeral JIT kernels for workloads

(c)Rupert Summerskill

*****

Maximus-Secure: dev/random for TPM, Secure Chips & SIM's & Operating Systems & Applications: (c)RS

Core principles for getting enough randomness into TPM, Secure Chips & SIM's

The right to prioritize seeding and unique identity:

Trust model: importing entropy is only safe if it’s authenticated, fresh, and auditable..

On the other hand, Auditing effectively means a man in the middle, Hash & Blend..

Treat remote entropy as additive, never as your sole source.

DRBG realities: a DRBG “stretches” entropy; it doesn’t create it..

Reseed routinely and account for how much true entropy you’ve injected since the last reseed.

Health and attestation: continuously test local noise sources and require attestation for any device that supplies seeds (ATM, HSM, host).

Privacy: avoid any global, correlatable “seed” that could fingerprint cards..

Derive per‑card secrets internally; Use external entropy only to improve unpredictability.

Crypto strength: AES‑256 and modern ECC/PQC are not to be "broken by watching traffic"..

Entropy architecture for credit‑card ASICs and TPM‑like chips:

Local entropy (primary):

SRAM startup state/PUF: capture early‑boot SRAM noise, whiten, and feed as initial entropy.

Jitter and timers: ring‑oscillator jitter, metastability loops, clock drift across domains.

System events: inter‑arrival timing of user/IO events, RF/EM side noise (if available).

Health tests: repetition count and adaptive proportion tests (per SP 800‑90B style) before accepting local samples.

Authenticated remote entropy (secondary):

Verifiable randomness (e.g., drand): fetch signed randomness; verify signatures and round freshness before use.

Pollinate‑style seed clients: only over mutually authenticated TLS with pinned roots; validate server identity and integrity of payloads.

ATM/HSM reseed channel: require mutual attestation (TPM quote or HSM device cert), then pass a seed blob over an encrypted, integrity‑protected APDU.

Transport hardening:

Mutual TLS: card presents a device cert; seed source presents its attested identity.

Freshness: include round numbers, nonces, and monotonic counters; reject replayed seeds.

Audit: log signed receipts (provider ID, time, bytes, hash) on the host side, and a compact hash/counter on the card.

Mixing and DRBG integration inside the chip:

Use the chip’s native stir/reseed API: if available, call a primitive like TPM2_StirRandom to inject external entropy into the internal pool, then reseed the DRBG that backs keygen and nonces.

Conservative mixing construction: combine inputs with a one‑way KDF before reseed.

DRBG choice and rules:

HMAC‑DRBG (SHA‑256/512) or AES‑CTR‑DRBG with prediction resistance enabled.

Reseed interval: tie to output volume and time (e.g., every session or N kilobytes), and after any failure of health tests.

(Show you are a cynic ôo), "4 Real Men"(tm):

Entropy accounting: treat remote seeds as contributing limited entropy unless the service provides a verifiable bound; never “credit” more than is justified..

Continuous tests: implement online health checks for the local noise stream and disable outputs on anomalies until reseed succeeds.

Seed storage, wear management, and identity:

Seed vault design:

Encrypted at rest: store seeds under a chip‑resident key (sealed to device state); add a MAC and anti‑rollback counters.

Wear‑leveling: journal updates and rotate across pages to stay within NAND endurance; batch reseeds to minimize writes.

Freshness metadata: store a compact record (counter, hash of seed, timestamp or epoch) for audit and replay defense.

Sizing guidance:

16 × 1KB (≈16KB) offline seeds can work for constrained designs, but avoid fixed “one per month” rules; tie to usage and health.

1MB with 4KB packing is generous for cards; only worth it if your write budget and power profile can handle it. Most designs succeed with tens of kilobytes plus authenticated online reseeds.

Erase discipline: once a seed is consumed and mixed, zeroize its plaintext; retain only the MAC’d log entry.

Unique ID derivation:

Derive per‑card identifiers from an internal master secret using a KDF and context labels (not from external entropy).

Never export raw identifiers; use privacy‑preserving tokens (blinded, per‑transaction pseudonyms) to avoid cross‑domain tracking.

Reseed protocols you can implement:

Cold‑start bootstrap, Source order:

Local: collect SRAM/PUF + jitter; pass health tests.

Remote: fetch one verifiable randomness block; verify signature and freshness.

Mix: HKDF mix local + remote; call chip stir; reseed DRBG; enable outputs.

Record: increment reseed counter; store MAC’d receipt and counter.

Periodic reseed (time/usage based)

Trigger: every T hours or after N KB of DRBG output.

Fetch: attempt remote entropy; if unavailable, rely on local noise only.

Attest: on ATM/HSM path, verify device quote/cert; include transaction nonce.

Mix & zeroize: as above; update counters; compress logs to minimize writes.

Transaction‑coupled reseed (at terminals)

Handshake: terminal and card run mutual attestation; establish an ephemeral secure channel.

Seed transfer: terminal sends signed seed + proof; card verifies and mixes; terminal logs receipt.

Privacy: ensure seed blobs are indistinguishable, non‑identifying, and unlinkable across transactions.

Corrections, sizing, and cryptographic choices:

Don’t plan around “AES breaks in 15 minutes.” Properly implemented AES‑256‑GCM with fresh nonces and sound keys is considered secure today,..

Failures come from poor randomness, nonce reuse, side channels, or key management, not passive traffic observation.

RSA‑512 is broken; use modern suites.. Right, Because not everyone can fit a 4096Bit RSA / ECC 512Bit

Prefer ECDHE with P‑256/P‑384 and authenticated signatures, or hybrid ECDHE+PQC where feasible..

If you adopt ML‑DSA (Dilithium) or other PQC, consider card CPU/RAM limits and APDU latency; hybrid Crypto is a pragmatic path.

DRAND & Pollinate SEED Storage strategy : Battery Backed Cache-RAM : RS

It has to be stated that,.. Battery backed S-RAM storage is a cloud privilege, Remember Cache-RAM does not have NAND Burnout!

Entropy rate: avoid “4KB per minute” targets.. Instead, enforce reseed on:

New sessions/transactions,

After N outputs (e.g., per 256KB–1MB),

On health test anomalies,

On attested remote seed availability..

You’ll meet confidentiality goals without over‑fetching or exhausting NAND.

Security pitfalls to avoid:

Unauthenticated seeds: never mix anything you haven’t verified.

Replay/rollback: protect with counters and freshness checks.

Correlation: don’t let external seed cadence or values fingerprint cards..

You hash it & add the bits, with DBRNG, cRNG & tRNG..

Single‑source dependence: maintain functionality if remote services are down; local noise + conservative DRBG must suffice.

Rupert Summerskill

*****

1. “QRNG in Python” vs true quantum entropy

What you’re using in Python today that calls itself “QRNG” but is maths based is, strictly speaking, a PRNG / DRBG with some “quantum‑inspired” branding. It’s still deterministic: same seed, same stream.

To get true quantum entropy you either need:

Hardware QRNG (on‑chip or external service): vacuum noise, photon statistics, etc.

Remote QRNG over an API, e.g. a QRNG service that streams raw or conditioned entropy.

In practice, for cards/TPMs/HSMs, the right pattern is:

Use QRNG as an entropy source (local or remote).

Feed that entropy into a DRBG or directly into crypto operations, depending on your certification and design.

The interesting bit is how to hook that into something like wolfSSL/wolfCrypt cleanly.

2. How wolfSSL’s direct entropy injection changes the game

The wolfSSL article you linked is basically the bridge between “we have QRNG bits” and “our PQC actually uses them.” It shows that:

Post‑quantum KEMs like ML‑KEM (Kyber) require high‑quality random bytes for key generation and encapsulation; randomness is not a side detail, it’s central to security.

wolfCrypt exposes a function (MakeKeyWithRandom) that can take randomness directly, bypassing the internal DRBG, so you can use your own random source (e.g. QRNG) for key material.

In other words: instead of seeding the library’s DRBG and hoping it is used correctly everywhere, you can say:

“For this ML‑KEM keygen, use these bytes from the QRNG as the randomness.”

The wolfSSL RNG layer is already modular, with standard DRBG‑based RNGs and support for external sources like netRandom..

Direct entropy injection is the missing piece that lets a QRNG be used surgically for the most security‑critical steps (keygen, KEM, signatures) instead of globally and opaquely.

That’s exactly what you’re gesturing at.

3. Can this fit in smart cards, TPMs, and HSMs?

Your numbers (≈24 KB RAM, ~1 MB flash) are tight but realistic. They’re in the same ballpark as small MCUs where wolfSSL has already benchmarked PQC:

wolfSSL has published ML‑KEM and ML‑DSA benchmarks on STM32 platforms: performance is acceptable for embedded environments,

Which suggests that controlled PQC plus a lightweight entropy client is feasible on similar constrained devices.

Within those constraints, the pattern that does look realistic is:

Keep the card/TPM as a DRBG consumer, not a full “quantum emulator.”

Add a minimal QRNG client or QRNG‑fed SE/TPM nearby (terminal, host, secure co‑processor).

Inject quantum entropy using wolfCrypt’s direct entropy hooks when generating keys or shared secrets.

For legacy cards that can’t be physically upgraded with hardware QRNG:

Firmware upgrade path:

Add a small module that accepts externally supplied entropy (from host, terminal, HSM).

Use that entropy with wolfCrypt’s direct randomness interfaces for keygen and KEM operations.

Keep the internal DRBG for non‑critical randomness or as a fallback.

You’re right that we can’t “bolt on” a hardware quantum device to an already issued card, but we can:

Upgrade terminal‑side (ATMs, POS, readers, HSMs) to be QRNG‑fed and

Use application protocols that push QRNG‑derived key material into card cryptographic workflows (e.g., ephemeral keys, mutual auth, session keys).

4. Software QRNG vs QRNG‑fed DRBG: what’s actually worth doing?
There are really three tiers here:

Pure software “quantum‑inspired” RNG (no hardware entropy):

Security: equivalent to a PRNG/DRBG; security is mathematical, not physical.

Value: mostly marketing, unless the construction provides some unusual robustness.

This does not give you QRNG’s main advantage (unpredictability from physical noise).

Hardware/remote QRNG → DRBG (standard pattern):

Use QRNG to seed/reseed a robust DRBG (e.g. a NIST‑style DRBG as in wolfSSL’s RNG layer).

Pro: fits nicely in FIPS and existing certification models.

Con: you lose some “purity” but keep almost all practical security gains.

Hardware/remote QRNG → direct entropy injection into keygen/KEM:

Use QRNG bytes exactly where the article suggests: MakeKeyWithRandom and similar hooks for ML‑KEM, keys, etc.

Pro: extremely clear and auditable “this key was generated with QRNG entropy.”

Con: must be carefully designed to avoid mis‑use and must pass certification scrutiny.

Your “Soft QRNGs we could manage” makes sense if we interpret it as “QRNG‑fed cryptography via software integration”, not “purely mathematical quantum emulation” which brings less real security gain.


Option Entropy provenance Resource cost Auditability Best use

Hardware QRNG physical quantum events moderate–high high root of trust / provisioning

Software QRNG (math) algorithmic / deterministic low medium DRBG expansion / non-root uses

DRBG seeded by QRNG hybrid: physical seed + deterministic expansion low high if logged everyday keygen, FIPS path

Direct injection (MakeKeyWithRandom) raw bytes from source low–medium audit depends on logging provisioning, attested key ceremonies

...

Option Entropy provenance RAM cost Auditability Best use

Hardware QRNG physical quantum events medium high root of trust, provisioning

Remote verifiable beacon (drand/pollinate) signed network randomness low high if logged periodic reseed, fallback

Software “QRNG” (math) deterministic algorithm very low medium DRBG expansion, non‑root uses

DRBG seeded by QRNG hybrid (physical seed + DRBG) low high everyday keygen, FIPS path

Direct injection (MakeKeyWithRandom) raw bytes from source low audit depends on logging provisioning, attested key ceremonies

Key considerations (quick guide)

Entropy root matters — true unpredictability requires physical sources; math-based generators are deterministic and must not be treated as roots.

DRBG as choke point — seed a vetted DRBG and use it for all runtime randomness to get forward/backtracking resistance and FIPS alignment.

Direct injection is a tool, not a default — wc_KyberKey_MakeKeyWithRandom() lets you inject QRNG bytes directly for special cases (provisioning, attestation) but bypasses DRBG protections.

Constrained devices fit the hybrid model — with 24KB RAM / ~1MB flash, store a small DRBG implementation, reseed from an external QRNG or platform TRNG when available.

Operational hygiene and health tests — monitor RNG health, reseed on fork/boot, and fail closed on entropy failures; wolfSSL has hooks and known RNG health issues to watch for.

Practical next steps (concrete)

Prototype: implement QRNG → seed DRBG → use DRBG for Kyber keygen, and compare with direct-injection for provisioning; measure RAM/flash and latency on your card firmware.

Audit & logging: add provenance tags and logs for reseed events; treat direct-injection as auditable, rate‑limited ceremony only.

Validation: run NIST SP 800‑90B style entropy assessments on any QRNG/TRNG you rely on and keep a fallback DRBG policy.

Mitigate known pitfalls: ensure reseed after fork/child processes and track DRBG status to avoid predictable outputs

Rupert Summerskill

*****

a clear, structured, technically grounded analysis of the document — focused on TPM‑class entropy ingestion, seed‑mixing architectures, remote randomness beacons, and how these ideas fit into real‑world cryptographic engineering.

I’ll keep it precise, respectful of your intent, and aligned with what modern security engineering considers feasible.

Executive Summary (1–3 sentences)

Your document outlines a comprehensive entropy‑supply architecture for TPMs, smartcards, and secure elements with extremely small RAM budgets.

The core idea — blend local TRNG/PUF noise with authenticated remote randomness (drand, Pollinate, HSM‑provided seeds) and feed it into a compact DRBG — is sound and aligns with modern best practices.

The challenge is not conceptual but engineering constraints: RAM, NAND endurance, attestation, and certification.

What your document gets exactly right

Here are the strongest, most technically aligned points in your design:

1. Local entropy must remain the root
You correctly emphasize that:

SRAM‑PUF startup noise

jitter loops

metastability

timer drift

…are the primary entropy sources for TPM‑class chips. This is exactly how real TPMs bootstrap their DRBGs.

2. Remote entropy must be authenticated and additive
Your insistence on:

signed drand beacons

authenticated Pollinate sources

HSM‑verified ATM entropy

…matches NIST SP 800‑90C guidance: external entropy must be verified and never replace local noise.

3. Mixing via HKDF/HMAC‑DRBG is correct

Your HKDF‑based mixing and whitening is cryptographically sound:

HKDF‑Extract collapses multiple entropy sources

HKDF‑Expand produces uniform key material

HMAC‑DRBG or CTR‑DRBG is the right deterministic core

This is exactly how modern hybrid entropy pools are built.

4. Your “drip driver” architecture is realistic
The idea of:

periodic reseeds

jittered timing

multi‑source blending

health checks

…is consistent with SP 800‑90B and Linux’s own RNG design.

5. Your RAM‑constrained firmware model is accurate
TPMs and smartcards do operate with:

~24 KB RAM

compressed firmware

segmented execution

tiny AES/SHA kernels in assembly

Your LZSS/heatshrink decompressor strategy is exactly what real secure elements use.

Where the design needs refinement (but is directionally correct)

1. “4 KB per minute” entropy targets are unnecessary
Modern DRBGs do not require continuous high‑rate entropy injection.

Instead, reseed:

at boot

per session

after N KB of DRBG output

after health‑test anomalies

This reduces NAND wear and network load.

2. Cards cannot store 1 MB of seeds
Even if flash is 1 MB total, only a fraction is available for seed vaults.
A realistic seed vault is:

8–32 KB total

journaled

MAC‑protected

wear‑leveled

Your “16 × 1 KB seeds” model is actually quite reasonable.

3. AES‑256 does not break in 15 minutes
Confidentiality failures come from:

nonce reuse

bad randomness

side channels

key leakage

Not passive observation.

Your architecture fixes the real problem: entropy quality, not AES strength.

4. ML‑KEM on 24 KB RAM is possible only with streaming
You correctly note:

Kyber768 public key = 1184 bytes

private key = 2400 bytes

ciphertext = 1088 bytes

A 24 KB device must:

stream from NAND

use partial buffers

keep only hot loops in RAM

This is feasible but requires careful engineering.

A realistic entropy architecture for TPM‑class chips,

Here is a concise, deployable version of what you’re proposing.

1. Local entropy (root)
SRAM‑PUF startup

jitter loops

metastability

clock drift

health tests (repetition/adaptive proportion)

2. Remote entropy (secondary)
drand (signature‑verified)

Pollinate (TLS‑authenticated)

ATM/HSM seed (mutual attestation)

3. Mixing
Use HKDF:

Code
IKM = local_trng || drand || pollinate || os_urandom || run_id
PRK = HKDF-Extract(device_salt, IKM)
OKM = HKDF-Expand(PRK, "card-seed-v1", 64)
4. DRBG
AES‑CTR‑DRBG or HMAC‑DRBG

prediction resistance enabled

reseed on usage/time/health triggers

5. Storage
encrypted seed vault (8–32 KB)

MAC + anti‑rollback counter

wear‑leveled pages

zeroize consumed seeds

6. Transaction‑coupled reseed
At ATM/POS:

mutual attestation

ephemeral secure channel

signed seed blob

mix + reseed

This is exactly how modern EMV kernels could evolve.

How wolfSSL fits into this

The reference to wolfSSL’s direct entropy injection is spot‑on.

The wolfSSL article explains that ML‑KEM randomness can be supplied directly to:

MakeKeyWithRandom

wc_KyberKey_MakeKeyWithRandom()

This allows:

QRNG‑fed keygen

remote‑entropy‑fed keygen

auditable provisioning ceremonies

It’s a perfect match for your architecture.

What the document ultimately proposes

You are describing a hybrid entropy ecosystem for the next generation of secure chips:

Local TRNG/PUF as the root

Remote verifiable randomness as additive entropy

HKDF‑based mixing

DRBG as the deterministic core

Seed vault with wear‑leveling

Streaming PQC on tiny RAM

wolfSSL direct entropy injection for PQC keygen

Attested terminals as entropy relays

This is not only feasible — it is exactly the direction the industry is moving.

Rupert Summerskill

*****

Architecture: entropy plumbing (high level)

Principle: Entropy is plural. Mix independent sources, verify provenance, and make the DRBG the single consumer choke point.

Primary sources: local TRNG (if present), SRAM/PUF at boot, ring‑oscillator jitter, and verified remote beacons (drand/pollinate).

Mixing: always mix inputs with a one‑way KDF (HKDF‑SHA256 or SHAKE‑128) before injecting into DRBG state. Never treat remote beacons as sole entropy.

DRBG: use a small, vetted DRBG (HMAC‑DRBG or AES‑CTR‑DRBG) as the runtime provider. Reseed policy must be explicit and auditable.

Direct injection: reserve for provisioning/attestation ceremonies; log provenance (beacon round, signature, monotonic counter) and rate‑limit.

Health tests: lightweight SP 800‑90B style checks (repetition counts, adaptive proportion) before accepting local samples.

Minimal firmware design for a 24 KB RAM card (concrete)
Memory map (example)
Resident decompressor: 2 KB

Crypto kernel (ASM AES/HMAC/HKDF): 6 KB

DRBG state & control: 128 B

Seed buffer / staging: 4 KB

Scratch / stack: 4 KB

DMA cache window / compressed blocks: 6 KB

Execution model
Store firmware and large tables compressed on flash; decompress only hot segments into a fixed execution window (4–8 KB).

Keep a single shared AES core in ASM used by CTR‑DRBG and HKDF/HMAC. Keep DRBG state tiny and deterministic.

Use secure DMA to stream compressed segments; verify MAC/signature before decompression. Batch seed writes to NAND to preserve endurance.

DRBG & reseed policy
DRBG choice: HMAC‑DRBG (SHA‑256) or AES‑CTR‑DRBG (AES‑256) with prediction resistance where feasible.

Reseed triggers: after N KB of output (e.g., 256 KB), on new session, after health test failure, or when a verified remote beacon is available.

Mixing: seed_material = HKDF(device_salt, beacon || local_trng || os_random || run_id) then call chip stir/reseed API. Zeroize inputs after use.

Health tests (lightweight)
Repetition test: reject if long runs of identical bytes.

Adaptive proportion: check frequency distribution for gross bias.

Monotonic counter: increment on each reseed; reject replayed seeds.

wolfSSL integration patterns (practical)
Pattern A — QRNG → seed DRBG → use wolfCrypt normally
Fetch verified beacon (drand/pollinate) and local TRNG sample.

Mix with HKDF to produce a seed blob.

Call wolfCrypt’s DRBG reseed API (or wc_RNG_Seed equivalent) with the seed blob.

Use wolfCrypt APIs for keygen/encapsulation; library uses its DRBG.

Pros: FIPS‑friendly, single choke point, forward/backtracking resistance.
Cons: less explicit per‑key provenance.

Pattern B — Direct injection for ML‑KEM keygen
Fetch QRNG bytes (sized to the KEM randomness requirement).

Verify beacon signature and record round ID.

Call wc_KyberKey_MakeKeyWithRandom() (or equivalent) passing the QRNG bytes directly.

Log provenance and increment monotonic counter.

Pros: auditable “this key used QRNG” statement.

Cons: bypasses DRBG protections; use only in ceremonies.

# fetch_beacon.py (concept)
import requests, hmac, hashlib, os

#PythonWolf

DRAND_URL = "https://api.drand.sh/public/latest" # example
def fetch_verified_drand():
r = requests.get(DRAND_URL, timeout=5)
r.raise_for_status()
j = r.json()
# j contains round, randomness (base64), signature; verify signature offline or with stored pubkey
randomness = bytes.fromhex(j['randomness']) # adapt to actual format
round_id = j['round']
return randomness, round_id

def derive_seed(beacon, trng, os32, device_salt):
ikm = beacon + trng + os32
prk = hmac.new(device_salt, ikm, hashlib.sha256).digest()
okm = b''
t = b''
i = 1
while len(okm) < 48:
t = hmac.new(prk, t + b"drip-v1" + bytes(), hashlib.sha256).digest()
okm += t; i += 1
return okm[:48] # use as seed material

#2 Reseed Wolf

/* pseudo-code */
#include <wolfssl/wolfcrypt/random.h>

void reseed_wolfrng(const uint8_t* seed, size_t seed_len) {
WC_RNG rng;
wc_InitRng(&rng);
/* If wolfCrypt exposes a reseed API, call it; otherwise reinitialize with seed */
wc_RNG_Seed(&rng, seed, seed_len); /* pseudo API */
/* Use rng for subsequent operations or set as global provider */
}

/* pseudo-code using wolfSSL API names as conceptual */
int kyber_keygen_with_random(const uint8_t *random_bytes, size_t random_len,
KyberKey *out_key) {
/* wc_KyberKey_MakeKeyWithRandom is the conceptual API */
return wc_KyberKey_MakeKeyWithRandom(out_key, random_bytes, random_len);
}

*

Firmware & operational checklist (practical): RS

Segment signing: sign compressed segments; verify before decompression.

Seed receipts: store compact MAC’d receipt (provider ID, round, hash) in NV; do not store raw seeds.

Anti‑rollback: monotonic counters for reseed events.

Wear management: batch writes and rotate pages.

Audit logs: host/HSM logs must include beacon round, signature, device quote (if used), and reseed counter.

Testing: run NIST SP 800‑90B style entropy assessments on local sources and validate remote beacon verification.

Certification: document mixing construction, health tests, and failure semantics for FIPS/CC review.

Performance & PQC practicalities

Memory: for Kyber/Dilithium on constrained devices, stream public/private key material from flash into a small buffer and operate on chunks; keep hot loops in decompressed cache.

Latency: prefetch and decompress PQC tables before transaction bursts.

Hybrid approach: use classical ECDHE + PQC hybrid KEMs where full PQC on card is too heavy; perform heavy PQC ops on host/HSM and use attested channels to the card.

Risks, mitigations, and hard rules
Never accept unauthenticated remote seeds.

Never treat software‑only “quantum” PRNG as an entropy root.

Fail closed on entropy health failures for keygen operations.

Limit direct injection to auditable ceremonies and require attestation for the source.

Avoid fingerprinting: mix per‑device salts and avoid exposing raw beacon cadence.

Deliverables I’ve included here
Compact architecture for constrained cards (memory map, decompression strategy, DRBG rules).

Integration patterns for wolfSSL: DRBG seeding and direct injection for ML‑KEM.

Prototype snippets (Python beacon fetch + seed derivation; conceptual C hooks).

Operational checklist for auditing, health tests, and certification.

Next practical moves you can take immediately
On a router / host: run the Python beacon fetch + derive_seed, then call wolfSSL reseed API or use direct injection for a test Kyber keygen. Measure RAM/latency.

On a card firmware: implement tiny decompressor + single AES/HMAC kernel in ASM, and a minimal DRBG state machine; test reseed and health checks in swtpm or emulator.

Validation: run entropy tests and produce an audit log format for reseed receipts (round, signature, counter, hash).

Rupert Summerskill