On the Vulnerability of Parameter-Level Defenses to Model Merging

• URL: http://arxiv.org/abs/2606.30360v1
• Date: Mon, 29 Jun 2026 14:26:13 GMT
• Title: On the Vulnerability of Parameter-Level Defenses to Model Merging
• Abstract summary: We propose the Anchor-Guided Attack (AGA) to circumvent existing safeguards.<n>AGA consistently bypasses both individual and composite defenses under realistic defense-agnostic scenarios.<n>We provide Anchor-Repulsive Fine-tuning (ARF) to mitigate the anchor dominance leveraged by AGA.
• Score: 75.62810606927121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized models without authorization. Recent works propose parameter-level defenses that employ linear parameter transformations to neutralize this threat. In this paper, we systematically analyze such defenses and reveal that their protected task vectors are inherently small in magnitude. Consequently, the protected weights remain overwhelmingly dominated by the pretrained model. Based on this observation, we designate the pretrained model as a static reference anchor and propose the Anchor-Guided Attack (AGA) to circumvent existing safeguards. Specifically, AGA aligns the protected model with this anchor to recover the transformation matrix analytically. Extensive evaluations validate that AGA consistently bypasses both individual and composite defenses under realistic defense-agnostic scenarios. Furthermore, we provide Anchor-Repulsive Fine-tuning (ARF), a defense method to mitigate the anchor dominance leveraged by AGA. Empirical results confirm that ARF effectively defeats the proposed attack. Our code is available at https://github.com/krumpguo/secure-merge-attack.

Related papers

• What Does It Mean to Break a Distillation Defense? [33.607700445538136]
  Black-box LLMs are vulnerable to distillation attacks, in which an attacker queries the model and trains a student on its outputs.<n>We propose a threat model framework that describes attackers along three dimensions: a query budget, a data budget, and an interface profile.<n>We argue that future work on distillation defenses, along with any governance or policy frameworks built around them, should explicitly specify and stress-test attacker capabilities.
  arXiv  Detail & Related papers  (2026-06-23T18:13:02Z)
• Dashed Line Defense: Plug-And-Play Defense Against Adaptive Score-Based Query Attacks [3.206339985805037]
  Dashed Line Defense (DLD) is a plug-and-play post-processing method specifically designed to withstand adaptive query strategies.<n>By introducing ambiguity in how the observed loss reflects the true adversarial strength of candidate examples, DLD prevents attackers from reliably analyzing and adapting their queries.<n>We provide theoretical guarantees of DLD's defense capability and validate its effectiveness through experiments on ImageNet.
  arXiv  Detail & Related papers  (2026-02-09T14:02:32Z)
• MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety [28.246225272659917]
  This paper introduces textbfMAGIC, a novel multi-turn multi-agent reinforcement learning framework.<n>It formulates Large Language Models safety alignment as an adversarial asymmetric game.<n>Our framework demonstrates superior defense success rates without compromising the helpfulness of the model.
  arXiv  Detail & Related papers  (2026-02-02T02:12:28Z)
• MARS: A Malignity-Aware Backdoor Defense in Federated Learning [51.77354308287098]
  Recently proposed state-of-the-art (SOTA) attack, 3DFed, uses an indicator mechanism to determine whether backdoor models have been accepted by the defender.<n>We propose a Malignity-Aware backdooR defenSe (MARS) that leverages backdoor energy to indicate the malicious extent of each neuron.<n>Experiments demonstrate that MARS can defend against SOTA backdoor attacks and significantly outperforms existing defenses.
  arXiv  Detail & Related papers  (2025-09-21T14:50:02Z)
• Benchmarking Misuse Mitigation Against Covert Adversaries [80.74502950627736]
  Existing language model safety evaluations focus on overt attacks and low-stakes tasks.<n>We develop Benchmarks for Stateful Defenses (BSD), a data generation pipeline that automates evaluations of covert attacks and corresponding defenses.<n>Our evaluations indicate that decomposition attacks are effective misuse enablers, and highlight stateful defenses as a countermeasure.
  arXiv  Detail & Related papers  (2025-06-06T17:33:33Z)
• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
  Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
  arXiv  Detail & Related papers  (2025-06-03T01:37:09Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

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.