pith:RRNQQP3V
CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering
Large language models achieve up to 64 points on a new benchmark for cryptographic binary reverse engineering, while human experts score 92.
arxiv:2604.03750 v2 · 2026-04-04 · cs.CR · cs.AI · cs.CL
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\pithnumber{RRNQQP3VOK7V2XZNRFM7UX3WPB}
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Claims
GPT-5.4, the best-performing model, achieves 64.03 out of 100 and recovers the flag in 59% of challenges. We also establish a strong human expert baseline of 92.19 points.
The 432 challenges, built from 48 algorithms and three insecure key-usage scenarios, accurately represent the distribution and difficulty of real-world cryptographic binary reverse engineering without introducing unintended biases in the evaluation framework.
CREBench benchmark finds frontier LLMs recover cryptographic flags in 59% of cases versus 92% for human experts.
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| First computed | 2026-08-07T00:45:49.438963Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RRNQQP3VOK7V2XZNRFM7UX3WPB \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8c5b083f7572bf5d5f2d8959fa5f767856528e26d8bc7dc534e76dc8a3986efa
Canonical record JSON
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