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cs.LG2024
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
Kevin Eykholt, Farhan Ahmed, Pratik Vaishnavi +1
The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade, resulting in a myriad of att…
cs.CR2024
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou +3
The increasing use of large language models (LLMs) trained by third parties raises significant security concerns. In particular, malicious actors can introduce backdoors through po…
cs.LG2024
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs
Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei +2
Large language models (LLMs) have shown to pose social and ethical risks such as generating toxic language or facilitating malicious use of hazardous knowledge. Machine unlearning…