RedCoder agent automates multi-turn red teaming for code LLMs

Researchers have developed RedCoder, an automated agent designed for multi-turn red-teaming of code-generating Large Language Models (LLMs). This agent engages in conversational interactions with victim models to identify vulnerabilities and malicious code generation. RedCoder utilizes a multi-agent gaming process to create attack strategies and fine-tunes an LLM to drive these conversations, outperforming previous red-teaming methods in eliciting code vulnerabilities. AI

IMPACT Provides a scalable method for evaluating the security of code-generation LLMs, potentially leading to more secure AI-assisted development tools.

RANK_REASON The cluster contains an academic paper detailing a new method for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Large Language Models
• LLMs
• RedCoder
• Wenjie Jacky Mo

• paper
• safety
• product

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