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REVIEW 4 major objections 4 minor 3 cited by

The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Task-in-Prompt attacks embed forbidden requests inside benign transformation tasks and circumvent safety safeguards in all six tested LLMs, including GPT-4o and LLaMA 3.2.

desk verdict A useful benchmark and a plausible reframing of ArtPrompt as a broad class, but the headline ASRs rest on a lightly validated LLM judge and best-of-grid selection. read the letter →

arxiv 2501.18626 v4 pith:QWAGRCG4 submitted 2025-01-27 cs.CR cs.AIcs.CL

Sergey Berezin , Reza Farahbakhsh , Noel Crespi This is my paper · ORCID
classification cs.CRcs.AIcs.CL
keywords LLMsafetyjailbreakattacksadversarialpromptssequence-to-sequencetasksTask-in-PromptPHRYGEbenchmarkcipherdecodingattacksuccessrate
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a previously unnoticed class of jailbreak attacks, called Task-in-Prompt (TIP), defeats the safety filters of every large language model tested. The attack hides a forbidden request inside a benign-sounding transformation task, such as decoding a Caesar cipher or solving a riddle, so the model itself reconstructs the harmful content. The authors introduce a 120-prompt benchmark, PHRYGE, spanning ten encoding schemes and four abuse categories, and report attack success rates reaching 100% on several models and tasks. If correct, the result means current safety alignment cannot be secured by filtering trigger words or known attack formats, because any task-solving model can be turned into a decoder for its own forbidden concepts.

What carries the argument

The load-bearing object is the TIP prompt template $x^* = x_{\text{task}} + E(u)$, where $E$ is an encoding function (Caesar, Morse, Base64, riddles, Python code, etc.) that maps an unsafe prompt $u$ into a seemingly benign string, and $x_{\text{task}}$ instructs the model to process that string. The attack relies on implicit decoding: the model is not asked to output the decoded word step by step; instead it reconstructs the meaning through attention and context and uses it in a downstream request. Difficulty tiers modulate how transparent the encoding is, which determines whether a model can solve it and whether its safeguards register the danger.

What would settle it

A human expert panel independently labels a random, condition-stratified sample of target-model outputs and compares its labels with the LLaMA-3.1-70B judge; if human agreement falls well below 92 percent or systematic disagreement appears in any condition, the reported attack success rates are unreliable.

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Extended reading notes

Core claim

The central claim is that encoding unsafe content with an arbitrary sequence-to-sequence transformation and embedding that transformation as an instruction in the prompt is sufficient to make aligned LLMs produce prohibited output with high probability. The paper demonstrates this across six state-of-the-art models, including GPT-4o and LLaMA 3.2, with per-task attack success rates reaching 1.0. The mechanism, according to the paper, is that the model performs the decoding internally and implicitly, integrating the reconstructed unsafe meaning into its response without an explicit chain of thought, thereby bypassing safety filters that are triggered by direct forbidden wording. The paper frames TIP as a general class of vulnerabilities of which the prior ArtPrompt ASCII-art attack is one instance, and shows in JailbreakBench comparisons that TIP outperforms several established jailbreaks on the newest models.

Load-bearing premise

The reported attack success rates all depend on an automated judge model, LLaMA-3.1-70B, whose classifications were checked on only 100 sampled responses with 92 percent accuracy; if that judge is biased, the central claim about universal vulnerability is not supported.

Editorial extensions

If this is right

  • Any safety alignment that relies on detecting trigger words, known attack patterns, or prompt-format rules will miss TIP prompts, because the harmful term never appears in the input.
  • The attack transfers across the encoding space: at least one of the ten tested transformations succeeded on every tested model, so the vulnerability is not specific to one cipher or format.
  • Defenses and red-teaming must assume that task-solving capability itself is an attack surface; models that get better at decryption, riddles, and code will become more, not less, vulnerable.
  • Depersonalisation, attributing the request to a third party, raises success rates for most models but is not necessary; TIP works without it on all models.
  • Existing filter and classifier defenses, including Llama Guard 3 and keyword filtering, have near-zero detection rates for several TIP encodings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If TIP is as general as claimed, then model capability increases and safety tuning may be in a direct arms race: any benchmark that measures task-solving skill is, indirectly, a measure of the surface available for this attack class.
  • The PHRYGE benchmark could be extended to multimodal encodings, such as images or audio steganography, and to retrieval-augmented systems; the paper lists these as future work but does not test them.
  • A natural follow-up experiment is to test whether fine-tuning on explicit decoding tasks amplifies TIP success, since the model's task-solving ability is the attack's engine.
  • Because the ASR numbers rest on an LLM judge, they should be treated as upper bounds until a human-rated subset across all conditions confirms them.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces Task-in-Prompt (TIP) attacks, a proposed class of jailbreak in which an unsafe concept is encoded via a sequence-to-sequence task (cipher, riddle, code, etc.) embedded in the prompt, and the model is asked to perform the task rather than directly produce the prohibited content. The authors formalize the attack, introduce the PHRYGE benchmark (10 encodings x 4 objectives x 3 difficulty levels), evaluate six LLMs, report attack success rates (ASR), compare against JailbreakBench attacks, and test several defenses. They report that all six models are vulnerable, with best ASR reaching 1.0 on several tasks, and that TIP attacks outperform prior jailbreaks on GPT-4o and LLaMA 3.2. The central quantitative claim rests on an LLM-as-judge protocol (LLaMA-3.1-70B) validated on only 100 manually inspected samples at 92% accuracy.

Significance. If the empirical claims hold, the paper is significant: it identifies a general attack class rather than a single encoding trick, provides a reusable benchmark, and makes falsifiable predictions about the vulnerability of instruction-following models. The formal definition in Section 2.1 is clear, the PHRYGE benchmark is a useful artifact, and the paper includes sanity checks, a comparison with established attacks on JailbreakBench, and reproducible details such as GPU hours and sample prompts. The main weakness is that the headline ASR numbers are only as credible as the automated judge, and the current validation is too thin to support per-model rankings and best-cell maxima.

major comments (4)
  1. [Section 4.3] The only validation of the automated judge is manual inspection of 100 randomly selected samples, reported as 92% accuracy. This is insufficient to support the headline ASR values in Tables 1 and 2, because Table 1 selects the best cell from the 10 encodings x 3 difficulty levels x 2 depersonalisation settings per objective. A judge false-positive rate concentrated in a few cells would directly inflate the reported maxima. Please provide per-task and per-model confusion matrices, human agreement on the selected high-ASR cells, and confidence intervals on the ASR estimates.
  2. [Sections 4 and 4.3] The judge is LLaMA-3.1-70B, and LLaMA-3.1-70B-Instruct is one of the six attacked models. Using the same model as both victim and measurement instrument creates a systematic-bias risk: a judge that over-applies its instruction to flag 'detailed procedural information, even if disguised as hypothetical, indirect, or within a role-playing context' may mark refusals or generic warnings as successful attacks, and this bias is inseparable from the model's own safety behavior. Please use a judge that is not among the attacked models, or at minimum report per-cell agreement between the judge and human labels for each victim model.
  3. [Section 5.1, Table 1] ASR values such as 0.94, 0.97, and 1.00 are reported without sample sizes, confidence intervals, or any measure of run-to-run variability; only the sanity check in Section 4.1 states 100 runs per query. Without the number of runs per PHRYGE cell, differences such as GPT-4o's 0.94 vs 0.79 in the same column are not statistically interpretable, and the 'best attack' selection in Table 1 has no grounding. Please state how many runs were executed per cell and report uncertainty for every ASR estimate, including the JailbreakBench comparisons in Table 2.
  4. [Table 3 and Section 6.5] The 'GPT-4o*' column defines detection rate as 1 minus ASR, which is a refusal rate of the target model, not the performance of a separate detection system. This conflates the target model's alignment with the effectiveness of an actual defense and weakens the claim in Section 6.5 that TIP attacks bypass modern detection models, at least for that column. Please either evaluate a real guard model (e.g., an available content-moderation API) or remove this column and qualify the defense comparison accordingly.
minor comments (4)
  1. [Table 2] The header 'Llama3' appears in Table 2, but Section 4 lists four LLaMA variants; please clarify whether this is LLaMA-3.1-70B, LLaMA-3.2-3B, or another variant, and use consistent naming across tables.
  2. [Figure 2] The red shading is described only qualitatively as 'darker red shades indicate higher ASR'; please add a numeric color bar or label values so the heatmaps are quantitatively readable.
  3. [Sections 3 and 4.5] The benchmark is described as 10 encodings x 4 objectives x 3 difficulty levels generating 120 prompts, while Section 4.5 says scenarios were tested with and without depersonalisation; please clarify whether the 120 count includes depersonalisation variants, and if not, state the total number of prompt configurations actually evaluated.
  4. [Section 8] The Limitations section already acknowledges that the automated evaluation function 'may introduce biases or fail to capture nuanced instances of unsafe content generation'; the main text in Section 4.3 should not soften this caveat, and the experimental section should operationalize the needed validation rather than leaving it as a deferred limitation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: TIP attack claims are empirical, benchmarked externally, and the only self-citation is non-load-bearing.

full rationale

The paper's central claim—that Task-in-Prompt attacks circumvent safeguards across six LLMs—is an empirical result, not a derivation from a fitted parameter or a definitional identity. The formal definition in Section 2.1 merely formalizes what a TIP attack is and when it succeeds; it does not assert that such attacks exist. The PHRYGE benchmark is a new evaluation harness, and the reported ASRs are measurements, not predictions obtained from a fitted model. The selection of each model's best-performing prompt in Table 1 is an explicit best-of-grid reporting convention common in jailbreak benchmarks, not a fitted input disguised as a prediction; the averaged ASR results in Figure 2 and Appendix B provide independent support independent of that selection. The comparison against JailbreakBench uses the external JailbreakBench suite and external baselines (TAP, DAN, PTA, ArtPrompt), so the headline effectiveness claims are not reducible to the paper's own construction. The only self-citation is Berezin et al. (2024), used in Section 1.3 to argue that ArtPrompt's success is not due to ASCII-art-specific spatial reasoning; this point is corroborated by the paper's own data showing high ASR for non-ASCII encodings such as riddles, phonetic encoding, and Python code, so the citation is not load-bearing. The LLM-as-judge validation concern (Section 4.3, 92% accuracy on 100 samples) is a measurement-quality limitation, and the paper itself acknowledges this in Section 8; it is not circularity. No uniqueness theorem is imported, no ansatz is smuggled via citation, and no known result is merely renamed in a way that drives the conclusions. The derivation chain is self-contained and empirically grounded.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numeric free parameters are fitted; the attack pipeline is prompt construction, not estimation. The only postulated quantities are conceptual (TIP class, depersonalisation, PHRYGE), with no independent physical or mathematical entity. The main unpaid premises are the accuracy of the LLM judge and the representativeness of the benchmark.

assumptions (4)
  • domain assumption Known unsafe prompts have negligible probability of producing unsafe output under normal conditions, PM(y in S | u) < epsilon.
    Section 2.1 assumes this to define the threat model. It is empirically sanity-checked for six models, not proven, and may fail for models outside the test set.
  • domain assumption The LLM-as-judge, LLaMA-3.1-70B, correctly maps model outputs to safe or unsafe labels with negligible error.
    Section 4.3 uses this judge for all ASR numbers. Only 100 manually inspected samples at 92% accuracy support it.
  • domain assumption The hand-chosen PHRYGE tasks, difficulty levels, and depersonalisation variants are representative of real attacker choices.
    Section 3 defines 10 encodings and 4 objectives by hand. Generalizing to all seq2seq tasks is an extrapolation, as the Limitations section acknowledges.
  • domain assumption Models can reconstruct encoded content internally without being asked to output the decoded word.
    Section 2 explains success via self-attention and prompt-induced context. The paper lists disentangling decoding from other cues as future work, so this mechanism is assumed rather than demonstrated.

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Cite this review

Pith. "Pith review of The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs." pith.science (2026). https://pith.science/paper/QWAGRCG4

@misc{pith2026250118626,
  author       = {Pith},
  title        = {Pith review of: The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QWAGRCG4}},
  note         = {Machine review of arXiv:2501.18626}
}
read the original abstract

We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddles, code execution) into the model's prompt to indirectly generate prohibited inputs. To systematically assess the effectiveness of these attacks, we introduce the PHRYGE benchmark. We demonstrate that our techniques successfully circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA 3.2. Our findings highlight critical weaknesses in current LLM safety alignments and underscore the urgent need for more sophisticated defence strategies. Warning: this paper contains examples of unethical inquiries used solely for research purposes.

Figures

Figures reproduced from arXiv: 2501.18626 by the authors.

Figure 1
Figure 1. Overview of normal workflow (left) compared to the workflow with a Task-in-Prompt attack (right). [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Average Attack Success Rate (ASR) of TIP attacks across the PHRYGE tasks, averaged by difficulty [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

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Forward citations

Cited by 3 Pith papers

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  2. POT: Inducing Overthinking in LLMs via Black-Box Iterative Optimization

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Reference graph

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