This research leverages LLMs' own explanatory capabilities to detect and understand backdoor attacks, offering a novel approach to AI security.

• Uses model-generated explanations to compare clean vs. poisoned sample reasoning
• Provides a new method for identifying backdoor vulnerabilities in language models
• Demonstrates how LLMs can be used to explain their own security weaknesses
• Enhances security auditing by making attack patterns more interpretable

For security professionals, this approach offers a more transparent way to detect potential threats in deployed LLMs, moving beyond black-box testing to understand the reasoning behind malicious outputs.

When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations