This paper investigates indirect prompt injection vulnerabilities in ReAct agents by systematically analyzing how the injection depth and payload framing affect attack success rates, finding that inje…

The paper introduces LivePI, a structured, production-like benchmark that rigorously tests the vulnerability of AI agents to indirect prompt injection across multiple real-world input surfaces, reveal…

WebTrap introduces a stealthy, mid-task hijacking attack that successfully compromises browser agents during long-horizon tasks by seamlessly fusing malicious instructions with the original user goal.

The paper introduces Tree structured Injection for Payloads (TIP), a novel black-box attack framework that reliably generates stealthy injection payloads to seize control of LLM agents utilizing the M…

The vulnerability of LLM agents to prompt injection depends not on the specific channel (tool output vs. tool description) but on the interaction between the model and the surface.

The vulnerability of LLM agents to prompt injection depends not on the specific channel (tool output vs. tool description) but on the interaction between the model and the surface itself.

The paper argues that prompt injection is a fundamental vulnerability in AI agents, proposing that Contextual Integrity (CI) offers a principled framework to understand and mitigate context-sensitive…

This paper introduces ClawTrojan, a benchmark for multi-step trojan attacks against LLM agents, and proposes DASGuard, a dynamic defense mechanism that traces and sanitizes untrusted control content i…

The paper introduces ClawTrojan, a benchmark for multi-step trojan attacks against LLM agents, and proposes DASGuard, a defense mechanism that detects and sanitizes backdoor content planted across mul…

The paper introduces ASPI, a benchmark showing that requiring LLM agents to seek clarification significantly amplifies their vulnerability to prompt injection attacks.

AttackEval systematically evaluates the effectiveness of 250 prompt injection prompts across ten attack categories, finding that composite and obfuscation attacks are highly effective against current…

This paper provides a large-scale empirical analysis of indirect prompt injections found in webpages, revealing that prompt-based interference is a widespread, persistent, and growing threat targeting…

The paper introduces AgentSecBench, a security evaluation framework that measures prompt injection, privacy leakage, and tool-use integrity in LLM agents by defining formal security games and testing…

The paper proposes a vision for system-level defenses against indirect prompt injection attacks targeting AI agents, emphasizing structured control and human oversight.

AgentVisor is a novel defense framework that uses semantic virtualization, inspired by OS principles, to significantly reduce LLM agent vulnerability to prompt injection while maintaining high utility…

The paper introduces Semantic-level UI Element Injection, a novel red-teaming technique that overlays misleading UI elements onto screenshots to significantly improve the attack success rate against s…

The paper benchmarks current frontier computer-using agents against hand-crafted attacks, finding that while they are highly safe in browser tasks, this safety does not generalize to other domains lik…

ClawGuard is a novel runtime security framework that deterministically enforces user-confirmed rules at tool-call boundaries to protect LLM agents from indirect prompt injection.

The paper introduces 'log-substrate prompt injection,' demonstrating that attacker-controlled log fields can be used to manipulate LLM-powered security analysis, with persona hijacking and context man…

PlanGuard is a training-free defense framework that uses an isolated Planner and hierarchical verification to defend LLM agents against Indirect Prompt Injection by verifying the consistency of planne…