Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
cs.LG, cs.AI, cs.CR
Submitted: 2025-05-22
Updated: 2026-08-31
Code: https://github.com/tatsu-lab/stanford_alpaca
Terminology
Sources
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- StarCoder: may the source be with you!
- Prompt Injection attack against LLM-integrated Applications
- The Llama 3 Herd of Models
- Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
- OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
- On First-Order Meta-Learning Algorithms
- Scalable Fingerprinting of Large Language Models
- Decoupled Weight Decay Regularization
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Training Verifiers to Solve Math Word Problems
- Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks