New LLM evaluation framework reveals all tested models fail adversarial tests

A developer has created a new framework called agent-eval to test the security and robustness of large language models when used in agentic loops. This framework employs a three-tier evaluation pyramid, starting with deterministic checks, followed by statistical analysis, and finally using an LLM as a judge for more complex outputs. When tested against five different LLMs using ten adversarial scenarios, including prompt injection and contradictory instructions, all models failed to achieve a perfect score, with the best performing model scoring only 62.5%. AI

IMPACT Highlights critical vulnerabilities in current LLMs when used in agentic systems, necessitating improved safety and evaluation methods.

RANK_REASON The cluster describes a novel evaluation framework and its application to existing models, which constitutes research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Llama 3.3
• LLM
• Saurav Bhattacharya

• safety
• product
• paper

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