Papers by Sindhu Padakandla
SafeQuant: LLM Safety Analysis via Quantized Gradient Inspection (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to jailbreak Large Language Models (LLMs) use computationally intensive verification or require adversarial fine-tuning, leaving models vulnerable to advanced attacks. |
| Approach: | They propose a framework that leverages quantized gradient patterns to identify harmful prompts efficiently. |
| Outcome: | The proposed framework outperforms existing defenses across multiple benchmarks while maintaining model utility. |