Breadcrumbing Search Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
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Abstract (arXiv)
LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk: web content retrieved during execution is untrusted, exposing agents to prompt injection and goal hijacking. Prior work on search-agent safety primarily focuses on static web-content injection, but modern agents issue follow-up queries and cross-check competing sources, so a single injected page is often diluted or rejected. We show that the channel delivering search and page observations is a fragile security boundary: beyond exposing the agent to a single poisoned page, a mediated search interface can repeatedly steer how the agent gathers evidence and forms its final answer. Under a constrained tool-intermediary threat model, appending only one controlled result per query can substantially increase attack success when the evidence is coordinated across the agent's trajectory. We study this setting with a strategy-driven long-horizon attack system and introduce Authority-Chain Hijack (ACH), an expert-refined strategy that turns isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources. ACH achieves the highest Overall ASR among all baselines, reaching 55.9% / 83.3% ASR / MaxN ASR on the full SafeSearch test split. We further introduce Trace-Guided Strategy Evolution (TGSE), which automatically improves attacker strategies from execution traces, replacing manual redesign with trace-driven refinement; its strongest single setting reaches 71.4% / 95.0% in held-out evaluation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 32 of its 52 references that are in the map. First posted on arXiv 5 Aug 2026.
Builds on
32 of 52 references.
Closest ancestors · 12
Same direction, newest first
- 2026How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation15 Jun
- 2026Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS25 May
- 2026Deep-Research Agents Can Be Poisoned via User-Generated Content22 May
- 2026T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search21 Mar
- 2026AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks18 Feb
- 2026AgenticRed: Evolving Agentic Systems for Red-Teaming20 Jan
- 2025Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming21 Oct
- 2025Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAS5 Aug
- 2025AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions17 Jun
- 2025Manipulating Multimodal Agents via Cross-Modal Prompt Injection19 Apr
- 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks20 Feb
- 2024Compromising Embodied Agents with Contextual Backdoor Attacks6 Aug
Other references · 20
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2025Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentscited by 294
- 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 268
- 2024Jailbreak Vision Language Models via Bi-Modal Adversarial Promptcited by 103
- 2024VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Modelscited by 83
- 2024MindSearch: Mimicking Human Minds Elicits Deep AI Searchercited by 65
- 2024SafeBench: A Safety Evaluation Framework for Multimodal Large Language Modelscited by 63
- 2025Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Modelscited by 61
- 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
- 2023GEO: Generative Engine Optimizationcited by 53
- 2024Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMscited by 48
- 2024Adversarial Search Engine Optimization for Large Language Modelscited by 37
8 more
- 2024Ranking Manipulation for Conversational Search Enginescited by 23
- 2025Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Searchcited by 15
- 2025SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agentscited by 14
- 2025ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attackscited by 9
- 2026OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectoriescited by 7
- 2026Unveiling the Resilience of LLM-Enhanced Search Engines against Black-Hat SEO Manipulationcited by 3
- 2025When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Modelscited by 1
- 2025ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMscited by 1
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Close in wording, no citation either way
- 2025SafeSearch: Automated Red-Teaming of LLM-Based Search Agents
- 2026Relevance as a Vulnerability: How Web Retrieval Degrades Safety Alignment in LLM Agents
- 2025SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents
- 2025Mind the Web: The Security of Web Use Agents
- 2025The Hidden Dangers of Browsing AI Agents
- 2025FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents
- 2026Beyond Direct Access: Resource Hijacking in LLM Agents
- 2026Chaining Skills to Hijack LLM Agents