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pith:RRNQQP3V

pith:2026:RRNQQP3VOK7V2XZNRFM7UX3WPB
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CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering

Baicheng Chen, Juanru Li, Tianxing He, Xiangru Liu, Yilei Chen, Yu Wang, Ziheng Zhou

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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1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

CREBench benchmark finds frontier LLMs recover cryptographic flags in 59% of cases versus 92% for human experts.

Cited by

1 paper in Pith

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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

8c5b083f7572bf5d5f2d8959fa5f767856528e26d8bc7dc534e76dc8a3986efa

Aliases

arxiv: 2604.03750 · arxiv_version: 2604.03750v2 · doi: 10.48550/arxiv.2604.03750 · pith_short_12: RRNQQP3VOK7V · pith_short_16: RRNQQP3VOK7V2XZN · pith_short_8: RRNQQP3V
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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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    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-04-04T14:51:09Z",
    "title_canon_sha256": "098aa7373ee783094b9792d99cf78b4032afabe1b7e17a4915c8e48820e39167"
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