arXiv:2606.18193v1 Announce Type: cross Abstract: We evaluate the adversarial robustness of two frontier large language models (LLMs) developed by Anthropic, Fable 5 and Opus 4.8, against four families of automated jailbreak attack across 7 826 harmful intents spanning a ten-category harm taxonomy. Using the HackAgent red-teaming framework, hundreds of thousands of adversarial attempts were generated and every apparent success was independently re-adjudicated by a panel of three judge models (majority vote). Both models resist the majority of attacks, but the residual surface is larger than ag
The rapid advancement and deployment of frontier LLMs necessitate ongoing, robust red-teaming efforts to identify and mitigate adversarial vulnerabilities before widespread adoption.
This study highlights that even state-of-the-art LLMs, like those from Anthropic, still possess significant 'residual surfaces' vulnerable to automated jailbreak attacks, posing risks to responsible AI deployment.
The explicit identification of specific models and methodologies for adversarial robustness evaluation provides actionable intelligence for developers to harden their systems and for policymakers to understand current limitations.
- · AI safety researchers
- · Red-teaming frameworks and tools
- · Governments focused on AI security
- · LLM developers (if they do not address vulnerabilities)
- · Users relying on unhardened AI systems
Increased pressure on LLM developers to invest more heavily in adversarial robustness and safety research.
Development of more sophisticated and adaptive red-teaming techniques as LLMs become more robust.
Potential regulatory requirements for mandatory, independent red-team assessments of frontier AI models prior to deployment.
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Read at arXiv cs.CL