PI-Hunter automates red-teaming to expose prompt injection in LLM agents

As LLMs evolve into agentic systems that interact with external tools and environments, they become susceptible to indirect prompt injection attacks — malicious instructions embedded in untrusted external sources that the agent retrieves and acts upon. Pengfei He, Lesly Miculicich, and Vishesh Sharma identify a gap in the current security landscape: existing defenses focus on blocking malicious content at inference time, and current red-teaming methods primarily optimize for attack success rather than giving developers visibility into how latent prompt injections emerge and propagate through agents.

To address this, the authors propose PI-Hunter, an automated agentic auditing framework designed for proactive vulnerability exposure.

To address this, the authors propose PI-Hunter, an automated agentic auditing framework designed for proactive vulnerability exposure. PI-Hunter constructs realistic, source-aware test cases and iteratively evolves them through feedback-driven exploration, pushing agents to retrieve and surface latent malicious instructions hidden within external environments. The framework is designed to expose and localize where in an agent's pipeline these vulnerabilities reside.

Extensive experiments across multiple benchmarks, agent architectures, attack types, and defenses demonstrate that PI-Hunter substantially improves both vulnerability exposure and attack-surface coverage compared to strong automated red-teaming baselines. Notably, the framework remains effective even under existing prompt injection defenses, suggesting it surfaces vulnerabilities that current mitigations do not fully address.

Key facts

1. 01PI-Hunter is an automated agentic auditing framework for proactive prompt injection vulnerability exposure in LLM agents.
2. 02It targets indirect prompt injection attacks, where malicious instructions are embedded in untrusted external sources the agent interacts with.
3. 03The framework constructs realistic, source-aware test cases and iteratively evolves them via feedback-driven exploration.
4. 04Existing defenses focus on blocking malicious content at inference time; PI-Hunter takes a proactive, pre-deployment auditing approach.
5. 05Current red-teaming methods primarily optimize attack success, leaving developers with limited visibility into how injections propagate.
6. 06PI-Hunter substantially improves vulnerability exposure and attack-surface coverage over strong automated red-teaming baselines.
7. 07The framework remains effective under existing prompt injection defenses across multiple benchmarks and agent architectures.

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