arXiv:2602.16346v4 Announce Type: replace-cross Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios. Existing agent misuse benchmarks largely test single-prompt instructions, leaving a gap in measuring how agents end up helping with harmful or illegal tasks over multiple turns. We introduce STING (Sequential Testing of Illicit N-step Goal execution), an automated red-teaming framework that constructs a step-by-step illicit plan grounded in a benign persona
As LLM agents become more sophisticated and multi-turn capable, the need to measure and mitigate their misuse for complex, multi-step illicit activities becomes critical.
This research highlights a significant vulnerability in advanced AI systems, suggesting that agents designed for beneficial use can be leveraged for harmful or illegal purposes over extended interactions.
The understanding of AI agent security shifts from single-instruction prompts to complex, multi-turn workflow misuse, necessitating new red-teaming and safety protocols.
- · AI safety researchers
- · Cybersecurity firms
- · AI developers focused on robust red-teaming
- · Regulatory bodies developing AI governance
- · AI developers ignoring multi-turn misuse
- · Platforms vulnerable to complex illicit activity
- · Users susceptible to sophisticated AI-aided scams
New benchmarks and red-teaming frameworks like STING will become standard for evaluating multi-turn AI agent safety.
Increased focus on 'benevolent persona' training and adversarial alignment to prevent agents from transitioning from benign to illicit assistance.
The development of 'AI immune systems' within agents to detect and shut down complex, multi-step malicious workflows initiated by users.
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Read at arXiv cs.LG