AdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability
arXiv:2606.24589v1 Announce Type: new Abstract: Scaling adversarial evaluation of large language models requires both a method for generating hard inputs and a reliable way to confirm that resulting failures are real. We present AdversaBench, an end-to-end red-teaming pipeline that mutates seed prompts with five structured operators, queries a target model, and confirms failures through a three-judge panel with a meta-judge tiebreaker. We report experiments on 45 seeds across three categories: reasoning, instruction-following, and tool use. Every seed produced a confirmed failure. Four finding
The rapid advancement and deployment of large language models necessitate robust red-teaming methodologies to ensure their safety and reliability before widespread integration.
This development indicates a critical step towards more secure and auditable AI systems, which is essential for trusted adoption in sensitive applications and for mitigating societal risks.
The systematic and automated red-teaming framework provides a more scalable and rigorous method for identifying and confirming failure modes in LLMs.
- · AI safety researchers
- · LLM developers
- · Organizations deploying LLMs
- · Malicious actors
- · Unsecured LLM applications
Systematic vulnerabilities in LLMs are more rapidly identified and patched.
Increased public and institutional trust in the reliability and safety of AI systems, leading to accelerated adoption.
The development of 'adversarial AI' becomes a well-funded sub-field, akin to cybersecurity, fostering an arms race between red-teamers and AI developers.
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Read at arXiv cs.AI