deadpoint 0.3.1

Purple-team RNG analysis toolkit: detect weak PRNG usage, prove exploitability by recovering generator state, and report the correct CSPRNG remediation.

deadpoint

A purple-team RNG analysis toolkit. Point it at a stream of "random" values from software or an embedded/OT device and it will tell you whether that randomness is predictable, prove it by recovering the generator's internal state and predicting future and past outputs, then tell you exactly what the code should have used instead.

DETECT  ->  EXPLOIT  ->  REMEDIATE      (blue -> red -> blue)

Demo

$ deadpoint predict tokens.txt --fmt hex --width 32 --forward 5 --backward 3
forward (5): [3387135577, 2892245552, 2493570821, 1340861552, 807034130]
backward (3): [2912693742, 3815118128, 1202786163]

$ deadpoint report tokens.txt --fmt hex --width 32 --forward 3 --backward 0
======================================================================
  DEADPOINT — RNG ANALYSIS REPORT
======================================================================
Risk rating      : CRITICAL
Samples analysed : 700 (32-bit words)

-- DETECT ------------------------------------------------------------
Verdict          : WEAK
Suspected family : MT19937
Confidence       : 0.99
Statistical tests: 5/5 passed (looks_random=True)

-- EXPLOIT -----------------------------------------------------------
State recovered  : True  (method: untemper + linear recurrence)
Predictions verified on holdout: True
Next outputs     : 3387135577, 2892245552, 2493570821

-- REMEDIATE ---------------------------------------------------------
[CRITICAL] Weak generator in use (MT19937)
      fix: Replace the generator with secrets / os.urandom for any
           security-relevant value (tokens, keys, nonces, IDs).

Non-cryptographic PRNGs (Mersenne Twister / MT19937, LCGs, xorshift) are still routinely used where unpredictability actually matters: session tokens, password reset tokens, "random" device IDs, challenge nonces, key generation on embedded gear. These generators are built for statistical quality and speed, not unpredictability — their state is fully recoverable from observed output (CWE-338, CWE-330, CWE-337). deadpoint demonstrates that, and then points you at the fix.

What makes it different from randcrack

Capability	randcrack	deadpoint
MT19937 from 624 clean getrandbits(32)	✅	✅
Partial / truncated outputs via an SMT (Z3) model	❌	✅ random(), getrandbits(k<32), randint/randrange
Rejection sampling (randint/randrange) handled exactly	❌	✅ via seed recovery + real-generator replay
Higher-level call modelling (random(), randint, …)	❌	✅
Backward prediction (rewind prior outputs)	❌	✅
Multi-family: LCG, Java Random + Math.random, PHP mt_rand, V8 Math.random	❌	✅
Seed recovery (small / Unix-time seeds) by replay	❌	✅
Detect + remediate stages, risk-rated report	❌	✅
Capture on-ramps (pcap, live endpoint) + optional Rust core	❌	✅
Embedded/OT randomness-audit framing	❌	✅

The Z3 partial-output solver is the headline: instead of needing 624 full 32-bit words, deadpoint builds a symbolic model of the MT19937 recurrence and tempering and solves for the state from whatever bits each call actually reveals.

Install

pip install deadpoint            # core (z3-solver, numpy)
pip install "deadpoint[remediate]"   # + cryptography for the secure AEAD helpers

Quickstart (library)

from deadpoint import ingest, analyze, MT19937Cracker, harden

stream = ingest("tokens.txt", fmt="hex", width=32)

report = analyze(stream)          # -> DetectReport
report.verdict                    # Verdict.WEAK
report.suspected                  # PRNGType.MT19937

cr = MT19937Cracker(call="getrandbits", nbits=32)
cr.feed(stream.values)
cr.recover()                      # untemper + state recovery
cr.predict(10)                    # next 10 outputs
cr.rewind(5)                      # 5 PRIOR outputs

# partial-output example: values came from random.randint(0, 999)
# randint/randrange use rejection sampling; deadpoint recovers a small/time seed
# and replays the REAL generator, so rejection is handled exactly.
cr = MT19937Cracker(call="randint", lo=0, hi=999)
cr.feed(observed_ints)
if cr.recover():          # exact via seed replay, or best-effort state solve
    cr.predict(3)

fixes = harden(report)            # -> RemediateReport (mappings + patches)

Quickstart (CLI)

deadpoint analyze tokens.txt --fmt hex --width 32
deadpoint predict outputs.txt --call randint --lo 0 --hi 999 --forward 5
deadpoint recover tokens.txt --fmt hex
deadpoint harden app.py --snippet app.py
deadpoint report device_dump.hex --fmt hex --out audit.txt

report runs the full pipeline and prints a risk-rated, plain-text audit (CRITICAL when state is recovered and predictions verify on a holdout); add --json anywhere for machine-readable output.

How recovery works

• Clean path. Each getrandbits(32) output is untempered back to its raw state word (the four tempering ops are bijections). 624 consecutive words clone the generator; the global output recurrence W[k+624] = W[k+397] ^ twist(W[k], W[k+1]) gives exact forward prediction, and inverting the twist recovers prior outputs (all but the single oldest per block — MT discards 31 bits each twist).
• Partial path. A Z3 model of the recurrence + tempering is constrained by the bits each call exposes (random() → 27+26 bits from two words; getrandbits(k) → top k bits; randint/randrange → reduced to getrandbits, best-effort because randbelow rejection-samples), and solved for the initial state. Every recovery is confirmed against a holdout the solver never saw, so a reported success is a proven one — and an unrecoverable case (e.g. a rejection landed in the window) returns cleanly instead of lying.
• Seeds. Small integer and Unix-time seeds are recovered by bounded brute force and confirmed by replay. For call-modelled streams this runs first (it's fast and exact): because it replays CPython's own generator, it handles randint/randrange rejection sampling — the "subtle case" — exactly, which the symbolic path only approximates.

Additional generators (stretch)

Beyond CPython's random, deadpoint recovers three more real-world generators:

• JavaScript / V8 Math.random (xorshift128+). A 128-bit state recovered from ~4 observed doubles via a small Z3 model, then exact forward prediction. Handles V8's 64-value cache reversal (xorshift.v8_unbatch).

  from deadpoint.exploit import xorshift
  cr = xorshift.V8Cracker(); cr.feed_and_recover(observed_doubles)
  cr.predict(5)                     # next Math.random() values
  

• Java Math.random / Random.nextDouble (48-bit LCG). Recovers the state from a single double by brute-forcing 2²² low bits, then predicts.

  from deadpoint.exploit import lcg
  st = lcg.recover_java_from_double(observed_double)
  lcg.java_double_predict(st, 5)
  

• PHP mt_rand (MT19937 mode). PHP output is temper(word) >> 1 — bit-for-bit CPython getrandbits(31) — so raw mt_rand() feeds straight into the Z3 solver. Includes a faithful engine, seed recovery, and range unscaling. (The legacy MT_RAND_PHP reload variant is intentionally out of scope.)

  from deadpoint.exploit import php
  cr = php.recover(raw_mt_rand_outputs)   # cr.predict(n) -> future mt_rand()
  

Capture on-ramps (pcap + live endpoints)

Beyond files/stdin, deadpoint can pull a value stream straight from a capture or a running service (both feed the same detect/exploit pipeline):

from deadpoint.pcap import extract_stream, offset_extractor
from deadpoint.web import sample_endpoint, regex_extractor

# Modbus transaction IDs at a fixed offset in each packet of a capture
stream = extract_stream("cap.pcap", offset_extractor(offset=2, nbytes=2), width=16)

# session tokens sampled live from an endpoint (fetch is injectable for testing)
stream = sample_endpoint("https://app.test/login", 700,
                         regex_extractor(r'"session":"([0-9a-f]+)"'), width=32)

Native acceleration (optional)

pip install deadpoint is pure Python — the published wheel contains no compiled code and has no Rust dependency. The native accelerator is strictly opt-in.

A Rust + PyO3 crate in native/ provides a compiled deadpoint_native module (untempering + clean state solve). To use it, build it yourself:

pip install maturin
cd native && maturin develop --release

deadpoint then imports it automatically and falls back to pure Python when it's absent — identical results either way, just faster. Check which path is active with python -c "from deadpoint.exploit import untemper as u; import importlib; print(importlib.import_module('deadpoint.exploit.untemper').NATIVE)". See native/README.md.

Diff-against-secrets demo

Run the same pipeline on a weak stream and a secrets stream side by side:

deadpoint demo --count 700

WEAK  — random                               | STRONG — secrets
  verdict    : WEAK                           |   verdict    : STRONG
  suspected  : MT19937                        |   suspected  : CSPRNG
  EXPLOIT    : state recovered, verified=True |   EXPLOIT    : not recoverable

One stream leaks its future; the other gives deadpoint nothing. That difference is secrets / os.urandom.

Interactive TUI (optional)

A Textual dashboard renders the whole pipeline live — detect verdict, exploit status, a forward/backward prediction table, and remediation — over any stream. It's an opt-in extra (a thin read-only view over the library; the core has no Textual dependency):

pip install "deadpoint[tui]"
deadpoint tui tokens.txt --fmt hex --width 32

q quits, r re-runs. For anything scriptable, prefer the CLI/library — the TUI is for interactive exploration.

Docs

• docs/usage.md — full CLI/library reference, formats, minimum sample counts.
• docs/case-study-ot-pairing-code.md — predicting an embedded device's "random" pairing code.
• docs/case-study-session-token-prediction.md — predicting web session tokens.

Non-goals (design rules, not footnotes)

• N1. deadpoint does not implement a new random number generator.
• N2. deadpoint implements no cryptographic primitive by hand. The remediation helpers wrap vetted libraries (secrets, cryptography's AEAD) and nothing else. "Don't roll your own crypto" is enforced in code — see deadpoint/remediate/secure_helpers.py, where every function's docstring states which vetted library it wraps.
• The tool breaks weak RNGs and recommends vetted ones — the exact inverse of rolling your own.
• Precise about families: glibc rand() is an additive-feedback generator, not a simple LCG, and is out of scope; the clean LCG targets are Java Random and textbook full-output LCGs.

Ethics / dual-use

deadpoint is an auditing / defensive-research tool for systems you own or are authorized to test. Every example here is self-assessment or authorized pentesting. Use it to prove a weakness so you can fix it — then fix it with secrets / os.urandom.

Development

pip install -e ".[dev]"
pytest -m "not slow"      # fast suite
pytest                    # includes the heavier Z3 random() recovery

Correctness is checkable against ground truth: property-based tests seed a known random.Random, run recovery, and assert predictions exactly match the reference generator's future and past outputs.

License

MIT — see LICENSE.