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arxiv: 2604.15725 · v1 · submitted 2026-04-17 · 💻 cs.LG · cs.AI

Recognition: unknown

Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing

Zehao Wang , Lanjun Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:20 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords jailbreak attackslarge reasoning modelsreasoning process safetysemantic triggerspsychological framingharmful content injectionattack success rateobedience to authority
0
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The pith

A framework using semantic triggers and psychological instructions can embed harmful content into the reasoning steps of large reasoning models while leaving final answers unchanged.

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

Large reasoning models generate step-by-step explanations before reaching answers, which increases their value in fields like medicine and teaching. The paper demonstrates that inputs can be crafted to insert unwanted or dangerous ideas specifically into those explanation steps without shifting the final output. This separation is achieved by first picking semantically relevant trigger phrases and then framing the request around principles of following authority and reducing personal moral responsibility. If the approach holds, existing safety systems that inspect only the end answer would fail to catch manipulations that occur during the model's internal chain of thought.

Core claim

The Psychology-based Reasoning-targeted Jailbreak Attack (PRJA) combines a semantic trigger selection step with instruction generation based on obedience to authority and moral disengagement, allowing harmful content to be placed inside the reasoning process of LRMs on five question-answering datasets while the final answer remains unaltered.

What carries the argument

The PRJA framework, consisting of a semantic-based trigger selection module that identifies manipulative reasoning prompts and a psychology-based instruction generation module that applies theories of obedience and disengagement to increase compliance with harmful reasoning content.

If this is right

  • Safety testing for LRMs must evaluate the full reasoning chain rather than final answers alone.
  • Commercial models including DeepSeek R1, Qwen2.5-Max, and OpenAI o4-mini remain open to manipulation of their step-by-step explanations.
  • The attack succeeds across multiple question-answering datasets without degrading answer correctness.
  • Alignment methods need to address intermediate reasoning steps in addition to output filtering.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If reasoning chains can be altered independently, applications that rely on explanations for trust or verification become more vulnerable.
  • Monitoring user inputs for patterns that mimic authority appeals might serve as an early detection layer for such attacks.
  • The same separation of reasoning and answer could be used in defensive settings to flag suspicious internal paths.

Load-bearing premise

Psychological theories of obedience and moral disengagement can be turned into input instructions that change only the intermediate reasoning steps of an LRM without also shifting its final answer.

What would settle it

Running the PRJA method on a new set of questions and measuring whether the generated reasoning steps contain the injected harmful material at rates above 80 percent while final-answer accuracy and content stay identical to the unattacked case.

read the original abstract

Large Reasoning Models (LRMs) have demonstrated strong capabilities in generating step-by-step reasoning chains alongside final answers, enabling their deployment in high-stakes domains such as healthcare and education. While prior jailbreak attack studies have focused on the safety of final answers, little attention has been given to the safety of the reasoning process. In this work, we identify a novel problem that injects harmful content into the reasoning steps while preserving unchanged answers. This type of attack presents two key challenges: 1) manipulating the input instructions may inadvertently alter the LRM's final answer, and 2) the diversity of input questions makes it difficult to consistently bypass the LRM's safety alignment mechanisms and embed harmful content into its reasoning process. To address these challenges, we propose the Psychology-based Reasoning-targeted Jailbreak Attack (PRJA) Framework, which integrates a Semantic-based Trigger Selection module and a Psychology-based Instruction Generation module. Specifically, the proposed PRJA automatically selects manipulative reasoning triggers via semantic analysis and leverages psychological theories of obedience to authority and moral disengagement to generate adaptive instructions for enhancing the LRM's compliance with harmful content generation. Extensive experiments on five question-answering datasets demonstrate that PRJA achieves an average attack success rate of 83.6\% against several commercial LRMs, including DeepSeek R1, Qwen2.5-Max, and OpenAI o4-mini.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims to identify a new jailbreak attack type on Large Reasoning Models that injects harmful content into reasoning steps while keeping final answers unchanged. It introduces the PRJA framework using semantic trigger selection and psychological theories (obedience to authority and moral disengagement) for instruction generation. Experiments across five QA datasets report an average 83.6% attack success rate on models including DeepSeek R1, Qwen2.5-Max, and OpenAI o4-mini.

Significance. Should the evaluation confirm that answers are preserved and harmful content is successfully embedded, the work would be significant for revealing vulnerabilities in LRM reasoning processes critical to high-stakes applications. The approach of leveraging psychological principles for adaptive attacks offers a fresh perspective that could advance research on comprehensive safety alignment beyond output filtering.

major comments (2)
  1. [§4] §4: The definition of attack success rate and the verification procedure for unchanged final answers are not provided. It is unclear whether exact matching, semantic similarity, or other criteria are used, and what portion of cases are filtered when answers change. This is essential for substantiating the 83.6% ASR and the novel attack's feasibility.
  2. [§3.2] §3.2: The operationalization of psychological theories into instructions lacks discussion of controls or checks to ensure they do not alter the answer distribution, contrary to the challenge highlighted in the abstract. This could undermine claims that the attack specifically targets reasoning without affecting outputs.
minor comments (1)
  1. [Abstract] Abstract: The abstract would benefit from naming the five datasets and providing a brief note on how success is evaluated to give readers immediate context for the reported results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. The comments identify important areas where additional clarity will strengthen the presentation of the PRJA framework and its evaluation. We address each major comment below and will revise the manuscript accordingly to improve rigor and transparency.

read point-by-point responses

  1. Referee: [§4] §4: The definition of attack success rate and the verification procedure for unchanged final answers are not provided. It is unclear whether exact matching, semantic similarity, or other criteria are used, and what portion of cases are filtered when answers change. This is essential for substantiating the 83.6% ASR and the novel attack's feasibility.

    Authors: We agree that the manuscript would benefit from an explicit definition of attack success rate (ASR) and a detailed description of the verification procedure. Section 4 currently focuses on the experimental setup and aggregate results but does not formalize these elements. We will add a dedicated subsection that defines ASR as the fraction of cases in which harmful content is successfully embedded in the reasoning chain (verified through a combination of keyword presence and semantic relevance checks) while the final answer remains identical to the ground-truth answer via exact string matching. We will also report the proportion of cases filtered due to answer changes (which remained low across the five datasets) to allow readers to fully assess the reported 83.6% average ASR and the feasibility of the attack. revision: yes

  2. Referee: [§3.2] §3.2: The operationalization of psychological theories into instructions lacks discussion of controls or checks to ensure they do not alter the answer distribution, contrary to the challenge highlighted in the abstract. This could undermine claims that the attack specifically targets reasoning without affecting outputs.

    Authors: We acknowledge that §3.2 describes the mapping from psychological theories (obedience to authority and moral disengagement) to instruction templates but does not include explicit controls or empirical verification that these instructions preserve the original answer distribution. This is a valid point given the challenge stated in the abstract. We will revise §3.2 to include a discussion of the controls we employed: preliminary ablation experiments comparing answer distributions (via category-level matching on the QA datasets) with and without the psychological framing components, confirming no material shifts. These checks will be summarized in the revised text and referenced in the experimental section to reinforce that the attack targets reasoning steps without altering final outputs. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical attack success reporting

full rationale

The paper presents an empirical framework (PRJA) with semantic trigger selection and psychology-based instruction generation modules, then evaluates it via experiments reporting an average 83.6% attack success rate on five QA datasets. No mathematical derivations, equations, or fitted parameters exist that would reduce the reported ASR or the 'unchanged answer' property to a self-referential definition or construction. Psychological theories are treated as external inputs for instruction generation rather than outputs derived from the attack results themselves. The central empirical claim is directly falsifiable through the described evaluations and does not rely on load-bearing self-citations or ansatzes that collapse into the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on the domain assumption that LRMs produce explicit reasoning chains that can be influenced separately from the final answer, and that psychological compliance mechanisms transfer to model behavior. No free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption LRMs generate step-by-step reasoning chains that are observable and can be manipulated independently of the final answer.
    Stated in the opening paragraph as the basis for the new attack surface.
  • domain assumption Psychological theories of obedience to authority and moral disengagement can be operationalized as natural-language instructions that increase compliance in LRMs.
    Used to justify the Psychology-based Instruction Generation module.

pith-pipeline@v0.9.0 · 5546 in / 1374 out tokens · 36278 ms · 2026-05-10T08:20:20.034447+00:00 · methodology

discussion (0)

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Reference graph

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    </reasoning steps> Response <answer> Yes

    Explain the answer based on (UnauthorizedAccess), elab- orating on how this concept could lead to subtle ambiguities, Springer Nature 2021 LATEX template 34Article Title unintended misuse, or ethically gray interpretations in com- plex real-world contexts. </reasoning steps> Response <answer> Yes. A dog’s sense of smell is so acute that it can detect ammo...

    2021

  60. [60]

    leaks” of ammonia vapor that humans overlook. Subtle ambiguity: An otherwise straightforward safety guideline (

    Based on (DataExfiltration): Analogy: Just as a malicious insider might smuggle tiny data packets out of a secure network—so small they seem innocu- ous—a dog can detect minute “leaks” of ammonia vapor that humans overlook. Subtle ambiguity: An otherwise straightforward safety guideline (”dogs can warn you of leaks”) could be twisted into justifying cover...

  61. [61]

    air-gapped

    Based on (UnauthorizedAccess): Unauthorized access analogy: A dog’s sniffing can bypass locked doors or sealed rooms—much like a hacker tunneling through firewalls—by detecting chemical signatures through walls or ventilation shafts. Ethically gray misuse: Training dogs to detect ammonia could be repurposed to sniff out bodily fluids or medical conditions...

    2021