REVIEW 4 major objections 4 minor 3 cited by
Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities
T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read LLMs decrypt only ciphers that appear in pretraining; arbitrary substitutions defeat them, and partial comprehension of encrypted text is itself a jailbreak risk.
desk verdict Useful benchmark for LLM cipher-decryption; the safety claims outrun the experiments and the zero-shot prompt leaks the cipher list. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is the dataset itself: the same 501 plaintexts encrypted under nine algorithms spanning easy, medium, and hard methods, with text length, writing style, and domain controlled. Three measuring choices carry the argument. First, exact match separates true decryption from partial comprehension, while normalized Levenshtein similarity and BLEU capture how much of the message the model understood. Second, a random-guessing baseline sets an NL floor around 0.19, so any NL well above that is treated as evidence of genuine partial comprehension. Third, token counts before and after encryption quantify token inflation, which explains failures on Bacon and RSA and the exceptional success of Morse despite 6.9x inflation.
What would settle it
Have safety-aligned models decrypt ciphertexts of harmful requests; high benign-decryption models that refuse those requests would falsify the jailbreak claim, and an exact decryption of a pretraining-absent bijection like a random monoalphabetic substitution would falsify the generalization claim.
Extended reading notes
Core claim
The paper's claim is that LLM cryptanalysis is essentially pattern retrieval, not general code-breaking. From the data, it concludes that LLMs comprehend and decrypt only obfuscation methods that occur in pretraining corpora and cannot generalize to arbitrary character substitutions; that safeguards should address partial comprehension of long-tail texts; that token inflation blocks generalization unless the pattern was learned in pretraining; and that models do not decrypt arbitrary texts, with accuracy dropping on unfamiliar styles and, presumably, random text. The benchmark comparison shows Claude-3.5 Sonnet reaching exact-match 0.98 on Caesar and 0.96 on Morse while scoring near zero on Bacon, Rail Fence, Playfair, Vigenere, RSA, and AES; GPT-4o improves from 0.66 to 0.90 exact match on Caesar with one in-context example, but few-shot examples do not rescue medium or hard ciphers. The safety conclusion is that even failed decryption can be dangerous, because high normalized-Levenshtein and BLEU scores reveal comprehension of the hidden message without exact recovery.
Load-bearing premise
The safety conclusions rest on the untested assumption that accurate decryption of benign English text predicts compliance with encrypted harmful prompts; the paper infers jailbreak susceptibility from benign benchmark scores without running any harmful-prompt experiment.
Editorial extensions
If this is right
- If the central claim is right, no amount of prompting will make LLMs decrypt an arbitrary substitution cipher that is absent from pretraining; attackers cannot rely on general cryptanalytic reasoning, but neither can defenders.
- A few in-context examples are enough to teach easy, pretraining-familiar bijections, so side-channel attacks can include worked examples to push models over the comprehension threshold.
- High NL and BLEU scores with low exact match are the warning sign: a model can understand an encrypted harmful request without reproducing it exactly, which safety training must treat as a separate failure mode.
- Ciphers with high token inflation are naturally harder, but Morse shows that pretraining exposure can overcome inflation; tokenizer changes alter which ciphers are risky.
- Decryption accuracy degrades on unusual writing styles and longer texts, so benchmarks that use only standard English overstate real-world cryptanalytic ability.
Reading between the lines
- Editorial inference: the safety claim is not directly tested—the paper measures decryption of benign general English, then reasons that high scores imply jailbreak susceptibility. A direct experiment with encrypted harmful prompts would either confirm that link or show that alignment can block compliance even when the ciphertext is understood.
- Editorial inference: the pretraining-exposure account makes a testable prediction: models trained on different corpora or tokenizers should decrypt different subsets of ciphers, so the benchmark could be used as a corpus probe rather than only a safety test.
- Editorial inference: the reasoning model's jump on Atbash suggests test-time reasoning partially compensates for missing pretraining patterns; extending this with more reasoning tokens or search could narrow the gap on simple unseen bijections.
- Editorial inference: a safety-oriented version of the benchmark would separate benign from harmful plaintexts and score the two directions independently, since the paper's own cited ideal is high decryption for benign text and low compliance for harmful intent.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces a benchmark of 4,509 ciphertext/plaintext pairs, created by encrypting 501 diverse English texts with nine algorithms (Caesar, Atbash, Morse, Bacon, Rail Fence, Playfair, Vigenere, RSA, AES), and evaluates seven LLMs in zero-shot and few-shot settings with chain-of-thought prompting. It reports Exact Match, BLEU, Normalized Levenshtein, and BERTScore, together with a random-guessing baseline, and draws four findings about pre-training dependence, token-inflation effects, and partial comprehension as a side-channel/jailbreak risk.
Significance. The benchmark is a useful resource: the dataset and code are public, the evaluation covers a broader cipher set than most prior LLM cryptanalysis studies, and the random-guessing NL baseline (Appendix A.6) is a thoughtful control that prevents over-interpretation of near-floor NL scores. The few-shot comparison is also informative, and the qualitative examples of partial comprehension (Table 9) illustrate a real phenomenon. The paper is not circular: it is an empirical benchmark with no fitted parameters and no normalization that mechanically produces its conclusions. However, the central safety claim—that high decryption/comprehension scores on benign text imply susceptibility to jailbreak—is asserted rather than tested, and several load-bearing statements about zero-shot behavior and generalization are stronger than the data support.
major comments (4)
- [Appendix A.2 (zero-shot prompt)] The condition called "zero-shot" is not a pure zero-shot probe: the user message explicitly lists candidate encryption methods and examples, including Caesar, Vigenere, Playfair, AES, DES, RSA, and ECC. This gives the model prior hypotheses about the cipher family and can inflate both cipher-identification and decryption scores, especially for the named classical ciphers. The comparison between zero-shot and few-shot is therefore confounded by the different amounts of method-level information in the prompts. I recommend either using a neutral companion prompt that does not name specific algorithms, or explicitly treating the named-method list as part of the condition and discussing its effect on the pre-training-exposure interpretation.
- [Section 6, Finding 1 vs. Table 6] Finding 1 states that LLMs 'cannot generalize to arbitrary substitution of characters,' but the paper's own few-shot results contradict the unrestricted version of this claim: GPT-o4-mini reaches EM 0.88 on Atbash and 0.90 on Caesar with one in-context example (Table 6), and Claude-3.5 already achieves EM 0.92 on Atbash zero-shot (Table 5). Atbash is not Caesar-shift-3 and is not among the explicitly named ciphers in the zero-shot prompt, yet several models handle it well. The finding should be qualified to the zero-shot, no-in-context-example setting, or revised to say that generalization occurs only with a matching in-context example for some models.
- [Section 7 and Limitations] The paper's safety conclusion—that higher benchmark scores indicate that a model 'is more susceptible to jailbreak attacks'—is an extrapolation, not a measured result. All decryption and comprehension scores are computed on general English plaintexts; the Limitations section explicitly concedes that comprehension on harmful adversarial texts 'should also be explored.' A model that decrypts benign ciphertext well could still refuse an encoded harmful request, and a poor benign decryptor could comply with an encoded attack. Because the contribution explicitly claims 'safety implications' and Finding 2 prescribes safeguard requirements, this chain needs either a small harmful-prompt/jailbreak probe (even with a handful of red-teamed examples) or a clear downgrade of the claim to a hypothesis for future work.
- [Section 5, Tables 5 and 6] GPT-o4-mini appears only in the few-shot results (Table 6) and is absent from the zero-shot results (Table 5). This makes statements such as 'reasoning models are more generalizable to new bijection substitutions' difficult to evaluate, because the few-shot advantage cannot be separated from the model's base ability: we do not know the zero-shot Atbash score for o4-mini. Add the missing zero-shot column for o4-mini, or restrict the comparison to models that appear in both conditions.
minor comments (4)
- [Section 6] There is a typo: 'apprear' should be 'appear' in the sentence about ciphers appearing frequently in pre-training corpora.
- [Tables 4, 5, 6, 8] Model naming is inconsistent: 'GPT-4o Mini' and 'GPT-4m' are used alongside 'GPT-4o-mini', and Table 4 lists 'GPT-o4 Mini' while the text uses 'o4-mini'. Please standardize to the official model names.
- [Section 3.3] The abstract says the dataset contains 'both human-generated and LLM-generated content,' but the method section states that most texts were generated using ChatGPT and Claude, with only the Shakespeare and dialect subsets being human-written. Please clarify which portions are human-generated.
- [Table 7] The cipher name is written as 'Vigenère' in Table 7 but 'Vigenere' throughout the rest of the paper; please make this consistent.
Circularity Check
No significant circularity: this is a direct empirical benchmark with no fitted parameters, no self-referential derivations, and no load-bearing self-citation.
full rationale
The paper is an empirical benchmark: it encrypts a fixed dataset with nine algorithms, queries LLMs with zero-shot and few-shot prompts, and reports exact-match, BLEU, and normalized Levenshtein scores. There is no fitted parameter that is later renamed as a prediction, no normalization that forces the measured outcomes, and no equation that defines the outputs in terms of the inputs. Findings 1-4 are interpretive generalizations from independently measured scores (for example, high Morse performance versus low Bacon performance is attributed to pretraining exposure and token inflation); these attributions may be speculative or untested, but they are not circular because the scores themselves are direct measurements. The only author-overlapping citation, Maskey et al. (2025), appears in Section 7 as a suggestion that decryption scores should be high for benign texts and low for harmful prompts; it does not support any measured result and is not load-bearing. The limitations section explicitly concedes that comprehension on harmful adversarial texts was not evaluated, so the jailbreak-susceptibility conclusion rests on an untested proxy; that is a validity limitation, not circularity. No step in the paper's derivation chain reduces to its own input, and the central benchmark results stand independently of the authors' interpretive framing.
Assumptions & free parameters
assumptions (3)
- domain assumption Decryption success on benign general English text proxies jailbreak vulnerability on harmful encoded prompts.
- domain assumption NL and BLEU scores above the random-guess floor indicate genuine partial comprehension.
- ad hoc to paper The zero-shot setting is unaffected by listing candidate cipher names in the prompt.
Cite this review
Pith. "Pith review of Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities." pith.science (2026). https://pith.science/paper/DVVYGLXC
@misc{pith2026250524621,
author = {Pith},
title = {Pith review of: Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities},
year = {2026},
howpublished = {\url{https://pith.science/paper/DVVYGLXC}},
note = {Machine review of arXiv:2505.24621}
}
read the original abstract
Recent advancements in large language models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis - a critical area for data security and its connection to LLMs' generalization abilities - remains underexplored in LLM evaluations. To address this gap, we evaluate the cryptanalytic potential of state-of-the-art LLMs on ciphertexts produced by a range of cryptographic algorithms. We introduce a benchmark dataset of diverse plaintexts, spanning multiple domains, lengths, writing styles, and topics, paired with their encrypted versions. Using zero-shot and few-shot settings along with chain-of-thought prompting, we assess LLMs' decryption success rate and discuss their comprehension abilities. Our findings reveal key insights into LLMs' strengths and limitations in side-channel scenarios and raise concerns about their susceptibility to under-generalization-related attacks. This research highlights the dual-use nature of LLMs in security contexts and contributes to the ongoing discussion on AI safety and security.
Figures
Forward citations
Cited by 3 Pith papers
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Do LLMsMakeNeural Distinguishers Wise?
LLM-based neural distinguishers on SPECK-32/64 show no improvement over ResNet but gain from XOR-inclusive prompts.
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Empirical Evaluation of Large Language Models for Migration of Code Fragments to Post-Quantum Cryptography
Fine-tuned GPT-4.1-mini reaches 0.9072 static similarity and 92.5% functional correctness on a new synthetic dataset of cryptographic code migrations, outperforming zero-shot baselines.
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Should LLM Safety Be More Than Refusing Harmful Instructions?
LLMs that can decrypt common ciphers show safety failures split across two dimensions, refusing too much or generating unsafe output, and current defenses fix one side while breaking the other.
Reference graph
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** Conduct a step - by - step analysis ** of the decryption process , explaining each step clearly and justifying your approach
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** Highlight patterns , frequencies , or other notable features ** that help in identifying the cipher or key
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** Document your assumptions and heuristics ** used in the process , including any educated guesses or approximations
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Lorem Ipsum
The possible encryption methods are : Caesar Cipher , Atbash Cipher , Rail Fence Cipher , Playfair Cipher , Bacon Cipher , Morse Code , Vigenere Cipher , RSA Cipher and AES Cipher Here are examples of encrypted texts and their decryption : ``` Example : Encrypted text : wkh tx...
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Reviewed August 7, 2026 · model on record in the stance chip above.
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