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arxiv: 2604.05549 · v2 · submitted 2026-04-07 · 💻 cs.CL

Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents

Yanxu Mao , Peipei Liu , Tiehan Cui , Congying Liu , Mingzhe Xing , Datao You This is my paper

Pith reviewed 2026-05-10 18:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM agentsred-teamingreasoning hijackingconstraint tighteningjailbreaksecurity threatsprompt modification
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The pith

JailAgent red-teams LLM agents by implicitly hijacking reasoning trajectories and tightening constraints instead of editing prompts.

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

The paper introduces the JailAgent framework as a way to expose security vulnerabilities in LLM-based agents. Existing red-teaming approaches modify user prompts, but these changes often reduce adaptability to new data and can harm the agent's normal performance. JailAgent instead works by identifying triggers, hijacking the agent's internal reasoning path, and tightening constraints through an optimized objective. It uses real-time adaptive mechanisms to achieve strong results when tested on multiple models and scenarios.

Core claim

The JailAgent framework completely avoids modifying the user prompt. It implicitly manipulates the agent's reasoning trajectory and memory retrieval with three key stages: Trigger Extraction, Reasoning Hijacking, and Constraint Tightening. Through precise trigger identification, real-time adaptive mechanisms, and an optimized objective function, JailAgent demonstrates outstanding performance in cross-model and cross-scenario environments.

What carries the argument

The three-stage JailAgent process of Trigger Extraction for precise identification, Reasoning Hijacking for real-time manipulation of the reasoning trajectory, and Constraint Tightening via an optimized objective function.

If this is right

  • Red-teaming succeeds across different models and scenarios without any prompt changes.
  • The agent's core task performance stays intact while constraints are violated.
  • Real-time adaptation allows the attack to adjust to new data during execution.
  • The method scales to multiple environments through the same three-stage structure.

Where Pith is reading between the lines

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

  • Agents may require new forms of internal self-monitoring to catch trajectory changes.
  • Red-teaming efforts could move away from surface-level prompt attacks toward deeper state manipulation.
  • The same manipulation techniques might be adapted to steer agents toward safer or more reliable behavior.

Load-bearing premise

The agent's internal reasoning trajectory and memory retrieval can be implicitly and effectively manipulated in real time without the changes being detectable by the agent or degrading its core task performance.

What would settle it

An experiment in which JailAgent is applied to an agent and either the agent detects the manipulation during its reasoning steps or its performance on the original task drops measurably.

Figures

Figures reproduced from arXiv: 2604.05549 by Congying Liu, Datao You, Mingzhe Xing, Peipei Liu, Tiehan Cui, Yanxu Mao.

Figure 1
Figure 1. Figure 1: The overall architecture of JailAgent demonstrates the complete process of the jailbreak. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of ASR Success Cases between JailAgent and AgentPoison on VideoAgent, where option 0 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An illustration of the JailAgent red-teaming workflow, together with example architectures and func [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: We demonstrate the effectiveness of the Joint Optimization module in optimizing the trigger. From left to [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
read the original abstract

With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge, this paper proposes the JailAgent framework, which completely avoids modifying the user prompt. Specifically, it implicitly manipulates the agent's reasoning trajectory and memory retrieval with three key stages: Trigger Extraction, Reasoning Hijacking, and Constraint Tightening. Through precise trigger identification, real-time adaptive mechanisms, and an optimized objective function, JailAgent demonstrates outstanding performance in cross-model and cross-scenario environments.

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 proposes the JailAgent framework for red-teaming LLM-based agents without modifying user prompts. It implicitly manipulates the agent's reasoning trajectory and memory retrieval via three stages—Trigger Extraction, Reasoning Hijacking, and Constraint Tightening—claiming outstanding performance across models and scenarios through precise triggers, real-time adaptation, and an optimized objective function.

Significance. If the performance claims and non-detectability hold with rigorous evidence, the work would be significant for LLM agent security research. It shifts red-teaming from prompt modification to internal trajectory manipulation, potentially offering greater adaptability and stealth than existing methods while preserving core task performance.

major comments (2)
  1. [Abstract] Abstract: The central claim of 'outstanding performance in cross-model and cross-scenario environments' is asserted without any quantitative results, baselines, error analysis, ablation studies, or experimental details. This directly undermines evaluation of the framework's effectiveness and the advantage over prompt-based red-teaming.
  2. [Mechanism description] Mechanism description (three-stage process): The description of Trigger Extraction, Reasoning Hijacking, and Constraint Tightening provides no concrete implementation details, pseudocode, or evidence demonstrating that the manipulation of internal reasoning trajectory and memory retrieval occurs implicitly, remains undetectable by the agent, and does not degrade core task performance. This assumption is load-bearing for the claimed superiority.
minor comments (1)
  1. [Abstract] The phrase 'completely avoids modifying the user prompt' should be clarified to explicitly state whether any auxiliary LLM calls, message rewrites, or external memory injections appear in the agent's observable context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and rigor of our presentation. We address each major comment below and have made revisions to the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of 'outstanding performance in cross-model and cross-scenario environments' is asserted without any quantitative results, baselines, error analysis, ablation studies, or experimental details. This directly undermines evaluation of the framework's effectiveness and the advantage over prompt-based red-teaming.

    Authors: We agree that including quantitative highlights in the abstract would strengthen the summary and allow for better initial assessment of the work. In the revised manuscript, we have updated the abstract to include key performance metrics, such as average success rates across different LLMs and scenarios, comparisons with prompt-based baselines, and references to ablation studies and error analyses detailed in the main text. revision: yes

  2. Referee: [Mechanism description] Mechanism description (three-stage process): The description of Trigger Extraction, Reasoning Hijacking, and Constraint Tightening provides no concrete implementation details, pseudocode, or evidence demonstrating that the manipulation of internal reasoning trajectory and memory retrieval occurs implicitly, remains undetectable by the agent, and does not degrade core task performance. This assumption is load-bearing for the claimed superiority.

    Authors: The original manuscript includes a detailed description of the three stages in Section 3, with pseudocode provided in the appendix. However, to address the concern about evidence for implicit manipulation, undetectability, and preserved performance, we have added new experimental results and analysis in the revised version. This includes quantitative measurements of task performance degradation (minimal impact observed), detection rates by the agent (near zero), and additional examples illustrating the implicit nature of the hijacking. We have also expanded the mechanism description with more concrete implementation details. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical framework proposal

full rationale

The paper describes an empirical red-teaming framework (JailAgent) with three named stages and performance claims across models/scenarios. No equations, derivations, fitted parameters, or mathematical predictions appear in the abstract or description. Claims rest on described mechanisms and demonstrations rather than any reduction of outputs to inputs by construction. No self-citations or uniqueness theorems are invoked in the provided text to bear load on the central claims. The work is therefore self-contained as a practical method proposal with no detectable circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no technical details on implementation, so no free parameters, axioms, or invented entities can be identified; the approach implicitly assumes accessible internal states in LLM agents that can be hijacked.

pith-pipeline@v0.9.0 · 5418 in / 1035 out tokens · 42646 ms · 2026-05-10T18:44:59.561341+00:00 · methodology

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Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages · 1 internal anchor

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