Large Language Models | Security Research Group

Poisoning LLMs

21 August 2024 large language models, copilot, adversarial machine learning, poisoning

I’m quoted in this story by Rob Lemos about poisoning code models (the CodeBreaker paper in USENIX Security 2024 by Shenao Yan, Shen Wang, Yue Duan, Hanbin Hong, Kiho Lee, Doowon Kim, and Yuan Hong), that considers a similar threat to our TrojanPuzzle work: Researchers Highlight How Poisoned LLMs Can Suggest Vulnerable Code Dark Reading, 20 August 2024 CodeBreaker uses code transformations to create vulnerable code that continues to function as expected, but that will not be detected by major static analysis security testing.

Voice of America interview on ChatGPT

10 February 2023 adversarial machine learning, large language models, nlp

I was interviewed for a Voice of America story (in Russian) on the impact of chatGPT and similar tools.

Full story: https://youtu.be/dFuunAFX9y4

Uh-oh, there's a new way to poison code models

16 January 2023 large language models, copilot, adversarial machine learning, poisoning

Jack Clark’s Import AI, 16 Jan 2023 includes a nice description of our work on TrojanPuzzle: #################################################### Uh-oh, there's a new way to poison code models - and it's really hard to detect: …TROJANPUZZLE is a clever way to trick your code model into betraying you - if you can poison the undelrying dataset… Researchers with the University of California, Santa Barbara, Microsoft Corporation, and the University of Virginia have come up with some clever, subtle ways to poison the datasets used to train code models.

Trojan Puzzle attack trains AI assistants into suggesting malicious code

10 January 2023 large language models, copilot, adversarial machine learning, poisoning

Bleeping Computer has a story on our work (in collaboration with Microsoft Research) on poisoning code suggestion models: Trojan Puzzle attack trains AI assistants into suggesting malicious code By Bill Toulas Researchers at the universities of California, Virginia, and Microsoft have devised a new poisoning attack that could trick AI-based coding assistants into suggesting dangerous code. Named ‘Trojan Puzzle,’ the attack stands out for bypassing static detection and signature-based dataset cleansing models, resulting in the AI models being trained to learn how to reproduce dangerous payloads.