Context: Large Language Models (LLMs) deployed in safety-critical domains face significant threats from backdoor attacks. Recent empirical evidence contradicts previous assumptions about attack scalability: poisoning attacks remain effective regardless of model or dataset size, requiring as few as 250 poisoned documents to compromise models from up to 13B parameters. This suggests data poisoning becomes easier, not harder, as systems scale.

Backdoors persist through post-training alignment techniques like Supervised Fine-Tuning and Reinforcement Learning from Human Feedback, compromising current defenses. However, persistence depends critically on poisoning timing and backdoor characteristics. Current verification methods are computationally prohibitive—Proof-of-Learning requires full model retraining and complete training transcript access. While step-wise verification shows promise for runtime detection, scalability to production models and resilience against adaptive adversaries remain unresolved.

Existing defenses focus on post-training detection rather than preventing attack success during training. Advancing data poisoning scaling dynamics—understanding how attack success correlates with dataset composition, poisoning density, and model capacity—is essential for developing evidence-based threat models and defense strategies.

Objective: This internship aims to empirically test and advance data poisoning attacks and defenses for LLMs through systematic experimentation and adversarial evaluation. Key responsibilities include: implementing state-of-the-art attack methods across multiple vectors (jailbreaking, targeted refusal, denial-of-service, information extraction); testing attacks on diverse model architectures and scales; establishing standardized evaluation protocols with metrics such as Attack Success Rate and Clean Accuracy; evaluating existing defenses, particularly step-wise verification; and developing reproducible test suites for objective defense benchmarking.

Requirements:

1. Background in computer science or a related field, with a focus on machine learning security, or adversarial machine learning.
2. Strong programming skills in languages commonly used for machine learning tasks (e.g., Python, C++).
3. Experience with machine learning systems, model training, or adversarial robustness is a plus.
4. Ability to work independently and collaborate in a research-driven environment.
5. Comfortable working in English, essential for documentation purposes.