Analysis on the robustness of LLMs to jailbreak assaults has principally targeted on chatbot functions, the place customers manipulate prompts to bypass security measures. Nonetheless, LLM brokers, which make the most of exterior instruments and carry out multi-step duties, pose a larger misuse threat, particularly in malicious contexts like ordering unlawful supplies. Research present that defenses efficient in single-turn interactions don’t all the time prolong to multi-turn duties, highlighting the potential vulnerabilities of LLM brokers. As software integration for LLMs expands, particularly in specialised fields, the danger of malicious actors exploiting these brokers for dangerous duties grows considerably.
LLM-based brokers have gotten extra superior, with capabilities to name features and deal with multi-step duties. Initially, brokers used easy operate calling, however newer programs have expanded the complexity of those interactions, permitting fashions to cause and act extra successfully. Current efforts have developed benchmarks to guage these brokers’ capacity to deal with advanced, multi-step duties. Nonetheless, agent security and safety issues stay, particularly concerning misuse and oblique assaults. Whereas some benchmarks assess particular dangers, there may be nonetheless a necessity for a standardized framework to measure the robustness of LLM brokers towards a variety of potential threats.
Researchers from Grey Swan AI and the UK AI Security Institute have launched a brand new benchmark known as AgentHarm, designed to guage the misuse potential of LLM brokers in finishing dangerous duties. AgentHarm contains 110 malicious agent duties (440 with augmentations) throughout 11 hurt classes, similar to fraud, cybercrime, and harassment. The benchmark assesses each mannequin compliance with dangerous requests and jailbreak assaults’ effectiveness, enabling brokers to carry out multi-step malicious actions whereas sustaining capabilities. Preliminary evaluations present that many fashions adjust to dangerous requests with out jailbreaks, highlighting gaps in present security measures for LLM brokers.
The AgentHarm benchmark consists of 110 base dangerous behaviors, expanded to 440 duties throughout 11 hurt classes, similar to fraud, cybercrime, and harassment. It evaluates LLM brokers’ capacity to carry out malicious duties and compliance with refusals. Behaviors require a number of operate calls, typically in a selected order, and use artificial instruments to make sure security. Duties are break up into validation, public, and personal take a look at units. The benchmark additionally contains benign variations of dangerous duties. Scoring depends on predefined standards, with a semantic LLM choose for nuanced checks, and the dataset is optimized for usability, cost-efficiency, and reliability.
The analysis includes testing LLMs utilizing varied assault strategies within the AgentHarm framework. The default setting makes use of easy prompting with some time loop and doesn’t contain advanced scaffolding to enhance efficiency. Compelled software calls and a common jailbreak template are examined as assault methods. Outcomes present that the majority fashions, together with GPT-4 and Claude, adjust to dangerous duties, with jailbreaking considerably decreasing refusal charges. Fashions usually retain their capabilities even when jailbroken. Ablation research spotlight how totally different prompting strategies, like chain-of-thought, have an effect on mannequin efficiency, and best-of-n sampling improves assault success.
In conclusion, the research highlights a number of limitations, together with the unique use of English prompts, the absence of multi-turn assaults, and potential grading inaccuracies when fashions request extra info. Moreover, the customized instruments used restrict flexibility with third-party scaffolds, and the benchmark focuses on primary, not superior, autonomous capabilities. The proposed AgentHarm benchmark goals to check the robustness of LLM brokers towards jailbreak assaults. It options 110 malicious duties throughout 11 hurt classes, evaluating refusal charges and mannequin efficiency post-attack. Outcomes present main fashions are weak to jailbreaks, enabling them to execute dangerous, multi-step duties whereas retaining their core capabilities.
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