Skip to content

Author

Minghong Fang

We have 6 of 17 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Preprint Sep 2026

Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning

Fine-tuning-as-a-service lets users adapt a safety-aligned language model to their own data, but it also creates a harmful fine-tuning attack surface: a small amount of harmful data mixed into an otherwise benign fine-tuning set can degrade the model's alignment. Two recent alignment-stage defenses address this problem...

Muhammad Zeeshan Akram, Mufid Kamel Marican, Anvesh Reddy Yenugu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Does the Unsafe Gradient Survive a Conversation? On the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue

Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns instead of a single prompt. Gradient-based jailbreak detectors such as GradSafe were developed for single prompts: they score an input by the alignment between its induced...

Omar Sheta, Rinku Deuja, Hadi Masoudi et al. · 0 citations
Jul 2026

Beware What You Autocomplete: Forensic Attribution of Backdoored Code Completions

Extensive evaluations across three representative vulnerability cases and ten backdoor attacks, along with sixteen competitive baselines, demonstrate that CodeTracer consistently achieves high forensic accuracy, low false identification rates, and strong robustness against adaptive attacks.

Anjun Gao, Yueyang Quan, Zhuqing Liu et al. · 2 citations
Preprint Aug 2026

NeuronGuard: Robust LLM Safety Alignment via Ablation-Aware Safety Signal Redistribution

A fine-tuning-stage defense that simultaneously hardens LLMs against both attack classes by redistributing safety signals across a broader set of neurons, and provides a formal guarantee that NeuronGuard strictly reduces the attack success rate (ASR) upper bound.

Anjun Gao, Yueyang Quan, Yu Xia et al. · 2 citations
Preprint Jul 2026

Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems

Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.

Yu Xia, Anjun Gao, Yueyang Quan et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.