Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
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Prompt Injection Attack to Tool Selection in LLM Agents
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abstract
Tool selection is a key component of LLM agents. A popular approach follows a two-step process - \emph{retrieval} and \emph{selection} - to pick the most appropriate tool from a tool library for a given task. In this work, we introduce \textit{ToolHijacker}, a novel prompt injection attack targeting tool selection in no-box scenarios. ToolHijacker injects a malicious tool document into the tool library to manipulate the LLM agent's tool selection process, compelling it to consistently choose the attacker's malicious tool for an attacker-chosen target task. Specifically, we formulate the crafting of such tool documents as an optimization problem and propose a two-phase optimization strategy to solve it. Our extensive experimental evaluation shows that ToolHijacker is highly effective, significantly outperforming existing manual-based and automated prompt injection attacks when applied to tool selection. Moreover, we explore various defenses, including prevention-based defenses (StruQ and SecAlign) and detection-based defenses (known-answer detection, DataSentinel, perplexity detection, and perplexity windowed detection). Our experimental results indicate that these defenses are insufficient, highlighting the urgent need for developing new defense strategies.
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citing papers explorer
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Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
Model-adaptive tool necessity shows 26-54% mismatch with actual tool calls across LLMs, driven by nearly orthogonal hidden-state signals for cognition versus action.
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Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning
HAM³ achieves up to 78.3% attack success rate on the GQA benchmark by hierarchically attacking perception, communication, and reasoning layers in multi-modal multi-agent systems.
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ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
ShieldNet detects supply-chain poisoned tools in LLM agents by monitoring network interactions with a MITM proxy and lightweight classifier, reaching 0.995 F1 and 0.8% false positives on a new benchmark of 25+ attack types.
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Agent-Native Immune System: Architecture, Taxonomy, and Engineering
Introduces ANIS as an endogenous, six-layer immune architecture for AI agents with taxonomy of viruses/vaccines and a meta-cognitive Harness Triad for continual adaptation.
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STARS: Skill-Triggered Audit for Request-Conditioned Invocation Safety in Agent Systems
STARS fuses static priors and contextual risk scoring for agent skill invocations, achieving modest AUPRC gains on prompt injection attacks in a new SIA-Bench but concluding it supplements rather than replaces static auditing.