Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection

Large language models (LLMs) have revolutionized natural language processing with their ability to generate coherent and contextually relevant text. However, their deployment raises significant concerns about the potential for generating harmful or inappropriate content. In this paper, we introduce Progressive Self-Reflection (PSR), a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically. Experimental results demonstrate that applying our proposed method to Llama-3.1-8B-Instruct reduces the attack success rate from 77.5\% to 5.9\%, to Llama-3.1-8B base from 89.7\% to 5.6\%, and to Qwen2.5-7B-Instruct from 44.4\% to 3.8\%, without additional training, while maintaining their original performance on benign tasks. Our approach acts as a test-time scaling method, where additional self-reflection rounds enhance safety at the cost of inference overhead. To balance safety with computational efficiency, we introduce a lightweight self-reflection predictor that estimates the optimal number of reflection rounds based on input complexity. This adaptive mechanism prevents unnecessary self-assessment on benign inputs while ensuring thorough evaluation when encountering potentially harmful content. Our findings suggest that Progressive Self-Reflection serves as a scalable test-time approach, enhancing LLM safety by dynamically allocating computational resources in proportion to the input's risk profile.

Key Contributions

• Progressive Self-Reflection (PSR): a training-free inference-time technique that interleaves generation with periodic self-assessment checkpoints using a binary harmful/harmless classifier on internal activations to trigger backtracking when harmful content is detected
• Lightweight MLP predictor that estimates the optimal number of reflection rounds per input based on complexity, avoiding unnecessary overhead on benign inputs
• Demonstrated significant ASR reductions across multiple LLMs (e.g., 77.5%→5.9% on Llama-3.1-8B-Instruct, 89.7%→5.6% on Llama-3.1-8B base) without any additional model training

🛡️ Threat Analysis

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