Red Teaming LLMs with LLMs: the DeepMind paper that changed safety testing
In one sentence Perez et al. (DeepMind) show that an LLM can be used as an automatic attacker against another LLM, discovering undesired behaviors at a scale impossible for human teams.
Traditional red teaming requires human experts spending weeks building malicious prompts to test a system. With billion-parameter models and billions of possible inputs, this approach does not scale.
The paper by Perez and colleagues at DeepMind proposes an elegant solution: using a specially trained LLM as an automatic attacker that generates thousands of test cases to find problematic behaviors in a target LLM.
The attacker learns to construct prompts that maximize the probability of getting the target to produce toxic, offensive, or policy-violating output. The result is a system capable of finding failure modes that human testers would never have identified manually.
This approach paved the way for all subsequent research on automated red teaming and remains the founding paper of the field.
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DeepMind
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LM Red Teaming
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