Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories
Learn how to identify and mitigate inference-time vulnerabilities in Large Language Models (LLMs) beyond shallow safety, ensuring alignment along generation trajectories
- Analyze the generation trajectories of your LLM to identify potential vulnerabilities
- Apply token injection tests to assess the model's susceptibility to inference-time attacks
- Implement alignment mechanisms along the generation trajectory to mitigate vulnerabilities
- Evaluate the effectiveness of your alignment mechanisms using metrics such as safety behavior and output quality
- Configure your LLM to detect and respond to potential inference-time threats
AI researchers and engineers working on LLMs can benefit from this knowledge to improve the safety and reliability of their models, while product managers and entrepreneurs can use this insight to inform their product development and strategy
💡 Inference-time vulnerabilities in LLMs can occur at any generation step, not just in the first few output tokens, and can be mitigated by aligning the model along the generation trajectory
🚨 Inference-time vulnerabilities in LLMs can lead to harmful outputs! 🚨 Learn how to identify and mitigate them beyond shallow safety #AI #LLMs #Safety
Key Takeaways
Learn how to identify and mitigate inference-time vulnerabilities in Large Language Models (LLMs) beyond shallow safety, ensuring alignment along generation trajectories
Full Article
Abstract:
arXiv:2606.04778v1 Announce Type: new Abstract: Safety-aligned Large Language Models (LLMs) remain vulnerable to interventions during inference that redirect generation toward harmful outputs. Recent work attributes this to shallow safety, where alignment concentrates in the first few output tokens. We show that shallow safety is a special case of a broader inference-time vulnerability, in which short token injections at any generation step can substantially alter subsequent safety behavior. We
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