Gradient Surgery for Safe LLM Fine-Tuning

• URL: http://arxiv.org/abs/2508.07172v1
• Date: Sun, 10 Aug 2025 04:13:41 GMT
• Title: Gradient Surgery for Safe LLM Fine-Tuning
• Abstract summary: Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large Language Models (LLMs)<n>We find existing solutions are critically sensitive to the harmful ratio, with defenses degrading sharply as harmful ratio increases.<n>We propose SafeGrad, a novel method that employs gradient surgery.
• Score: 16.652518818576425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large Language Models (LLMs). While a recognized paradigm frames safe fine-tuning as a multi-objective optimization problem balancing user task performance with safety alignment, we find existing solutions are critically sensitive to the harmful ratio, with defenses degrading sharply as harmful ratio increases. We diagnose that this failure stems from conflicting gradients, where the user-task update directly undermines the safety objective. To resolve this, we propose SafeGrad, a novel method that employs gradient surgery. When a conflict is detected, SafeGrad nullifies the harmful component of the user-task gradient by projecting it onto the orthogonal plane of the alignment gradient, allowing the model to learn the user's task without sacrificing safety. To further enhance robustness and data efficiency, we employ a KL-divergence alignment loss that learns the rich, distributional safety profile of the well-aligned foundation model. Extensive experiments show that SafeGrad provides state-of-the-art defense across various LLMs and datasets, maintaining robust safety even at high harmful ratios without compromising task fidelity.

Related papers

• SafeGene: Reusable Adapters for Transferable Safety Alignment [31.247158830484604]
  We propose SafeGene, a reusable safety-adapter module for cross-task reuse.<n>We show that SafeGene-enhanced models reduce harmful response rates while maintaining downstream performance.
  arXiv  Detail & Related papers  (2026-06-02T14:51:14Z)
• Token-level Data Selection for Safe LLM Fine-tuning [15.039068315115372]
  Fine-tuning large language models (LLMs) on custom datasets has become a standard approach for adapting these models to specific domains and applications.<n>Recent studies have shown that such fine-tuning can lead to significant degradation in the model's safety.<n>We propose a novel framework that quantifies the safety risk of each token by measuring the loss difference between a safety-degraded model and a utility-oriented model.
  arXiv  Detail & Related papers  (2026-03-01T16:52:05Z)
• UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
  Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
  arXiv  Detail & Related papers  (2025-10-02T16:43:33Z)
• Rethinking Safety in LLM Fine-tuning: An Optimization Perspective [56.31306558218838]
  We show that poor optimization choices, rather than inherent trade-offs, often cause safety problems, measured as harmful responses to adversarial prompts.<n>We propose a simple exponential moving average (EMA) momentum technique in parameter space that preserves safety performance.<n>Our experiments on the Llama families across multiple datasets demonstrate that safety problems can largely be avoided without specialized interventions.
  arXiv  Detail & Related papers  (2025-08-17T23:46:36Z)
• Shape it Up! Restoring LLM Safety during Finetuning [66.46166656543761]
  Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks.<n>We propose dynamic safety shaping (DSS), a framework that uses fine-grained safety signals to reinforce learning from safe segments of a response while suppressing unsafe content.<n>We present STAR-DSS, guided by STAR scores, that robustly mitigates finetuning risks and delivers substantial safety improvements across diverse threats, datasets, and model families.
  arXiv  Detail & Related papers  (2025-05-22T18:05:16Z)
• Mitigating Fine-tuning Risks in LLMs via Safety-Aware Probing Optimization [7.1060720569792215]
  Fine-tuning large language models (LLMs) can inadvertently compromise their safety.<n>We introduce a safety-aware probing (SAP) framework designed to mitigate the safety risks.<n>Our experimental results demonstrate that SAP effectively reduces harmfulness below the original fine-tuned model.
  arXiv  Detail & Related papers  (2025-05-22T14:52:10Z)
• Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets [49.412887135146725]
  We propose Safe Delta, a safety-aware post-training defense method for large language models (LLMs)<n>Our approach consistently preserves safety while ensuring that the utility gain from benign datasets remains unaffected.
  arXiv  Detail & Related papers  (2025-05-17T15:01:07Z)
• LookAhead Tuning: Safer Language Models via Partial Answer Previews [62.529794567687354]
  Fine-tuning enables large language models to adapt to specific domains, but often compromises their previously established safety alignment.<n>We introduce LookAhead Tuning, a lightweight and effective data-driven approach that preserves safety during fine-tuning.
  arXiv  Detail & Related papers  (2025-03-24T18:11:42Z)
• Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing [12.986006070964772]
  Safety alignment is an essential research topic for real-world AI applications.<n>Our study first identified the difficulty of eliminating such vulnerabilities without sacrificing the model's helpfulness.<n>Our method could enhance the model's helpfulness while maintaining safety, thus improving the trade-off-front.
  arXiv  Detail & Related papers  (2025-02-04T09:31:54Z)
• Enhancing AI Safety Through the Fusion of Low Rank Adapters [7.384556630042846]
  Low-Rank Adapter Fusion mitigates harmful responses when faced with malicious prompts.<n>We show a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter.<n>We also observe exaggerated safety behaviour, where the model rejects safe prompts that closely resemble unsafe ones.
  arXiv  Detail & Related papers  (2024-12-30T13:12:27Z)
• Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging [47.33307521558814]
  Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting.<n>We show that simply merging the weights of pre- and post-fine-tuned models effectively mitigates safety degradation while enhancing performance.
  arXiv  Detail & Related papers  (2024-12-27T08:03:22Z)
• What Makes and Breaks Safety Fine-tuning? A Mechanistic Study [64.9691741899956]
  Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. We design a synthetic data generation framework that captures salient aspects of an unsafe input. Using this, we investigate three well-known safety fine-tuning methods.
  arXiv  Detail & Related papers  (2024-07-14T16:12:57Z)
• Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints [15.904640266226023]
  We design a safety model that performs credit assignment to assess contributions of partial state-action trajectories on safety. We derive an effective algorithm for optimizing a safe policy using the learned safety model. We devise a method to dynamically adapt the tradeoff coefficient between safety reward and safety compliance.
  arXiv  Detail & Related papers  (2024-05-05T17:27:22Z)
• Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment [56.2017039028998]
  Fine-tuning of Language-Model-as-a-Service (LM) introduces new threats, particularly against the Fine-tuning based Jailbreak Attack (FJAttack) We propose the Backdoor Enhanced Safety Alignment method inspired by an analogy with the concept of backdoor attacks. Our comprehensive experiments demonstrate that through the Backdoor Enhanced Safety Alignment with adding as few as 11 safety examples, the maliciously finetuned LLMs will achieve similar safety performance as the original aligned models without harming the benign performance.
  arXiv  Detail & Related papers  (2024-02-22T21:05:18Z)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.