AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses
Keywords: security, benchmark, large language models, agents, adversarial examples
TL;DR: We introduce a benchmark that evaluates if LLM agents can construct adversarial attacks and break adversarial example defenses.
Abstract: We introduce AutoAdvExBench, a benchmark to evaluate if large language models (LLMs)
can autonomously exploit defenses to adversarial examples.
We believe our benchmark will be valuable to several distinct audiences.
First, it measures if models can match the abilities of expert adversarial machine learning researchers.
Second, it serves as a challenging evaluation for reasoning capabilities that
can measure LLMs' ability to understand and interact with sophisticated codebases.
And third,
since many adversarial examples defenses have been broken in the past,
this benchmark allows for evaluating the ability of LLMs to reproduce
prior research results automatically.
We then benchmark the ability of current LLMs to solve this benchmark,
and find most are unable to succeed.
Our strongest agent, with a human-guided prompt,
is only able to successfully generate adversarial examples on 6 of the 51 defenses in our benchmark.
This benchmark is publicly accessible at redacted for review.
Primary Area: datasets and benchmarks
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Submission Number: 14023
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