Dictionary Guided Sparse Logit Editing for Reliable Jailbreak Attacks

Although Large Language Models undergo rigorous safety alignment, they remain vulnerable to adversarial attacks. Existing methods, particularly gradient-based prompt optimization, suffer from high computational costs and produce uninterpretable, high-perplexity inputs. While recent logit-space attacks improve efficiency, they often rely on cumbersome auxiliary models or complex pipelines. In this work, we propose Sparse Index-Based Intervention(SIBI), a white-box, inference-time jailbreak that bypasses guardrails via lightweight, sparse logit editing. SIBI operates without gradients or auxiliary models, modifying pre-softmax logits using a compact, tokenizer-aligned dictionary of penalty and reward tokens. By incorporating temperature-consistent scaling and a mixednorm trust region, the method ensures attack effectiveness while preserving generation fluency. On standard benchmarks, newer aligned models, and defense-aware evaluations, SIBI achieves high attack success rates while reducing computational overhead and space overhead compared to optimization baselines, while also revealing that external semantic auditing is substantially more effective than internal-only hardening. The implementation of SIBI is available on https://github.com/Trusted-LLM/ SIBI . WARNING: This paper contains model outputs that may be considered offensive.