CrACK: Adversarial Attacks on Cross-Model Consistency in Collaborative Vision Foundation Models

• URL: http://arxiv.org/abs/2609.07499v1
• Date: Mon, 07 Sep 2026 13:47:33 GMT
• Title: CrACK: Adversarial Attacks on Cross-Model Consistency in Collaborative Vision Foundation Models
• Abstract summary: Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense prediction.<n>We propose CrACK, an inference-time attack that exploits this interface without modifying any input pixel, model weight, or training data.<n>Experiments show that CrACK causes catastrophic degradation while every individual model continues to produce its unchanged standalone output.
• Score: 16.64134816858303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense prediction and are increasingly deployed in safety-critical applications. The security of these systems is commonly assumed to follow from the robustness of their individual models. We challenge this assumption. We identify a vulnerability shared by every collaborative pipeline: each model consumes the intermediate output of another without verifying semantic consistency, an unverified premise that we term the semantic-spatial alignment dependency. Existing adversarial attacks target a single model and overlook this premise, leaving the inter-model interface entirely unguarded. We propose CrACK (Cross-model Adversarial Consistency attack), an inference-time attack that exploits this interface without modifying any input pixel, model weight, or training data. CrACK operates in two stages: Adversarial Affinity Contradiction Injection corrupts the cross-modal affinity matrix by inverting SAM encoder features under the guidance of CLIP patch-level semantics, and Semantic Interface Poisoning steers the prediction through a max-distance label permutation derived from CLIP text embeddings. Experiments on four collaborative pipelines across eight benchmarks show that CrACK causes catastrophic degradation while every individual model continues to produce its unchanged standalone output, rendering per-model defenses structurally blind. The corruption further cascades into large vision-language model reasoning, driving models such as LLaVA to produce erroneous responses from visually intact inputs. Our results show that the security of a collaborative AI system cannot be reduced to the robustness of its components, and that inter-model feature interfaces must be treated as first-class security boundaries.

Related papers

• Safe Vision Language Action Models via Barrier Enhanced Flow Matching [0.0]
  This article presents a modular inference framework that integrates Flow Matching generative models with formal Control Barrier Function safety guarantees.<n>By employing a smooth Log-Sum-Exponential aggregate barrier, we enforce safety over entire action chunks.<n>We validate the approach on two robotic manipulation platforms and a 2D navigation benchmark, verifying that our framework achieves reliable safety without degrading the success rate of the model.
  arXiv  Detail & Related papers  (2026-07-31T15:56:19Z)
• Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model [73.59026525350808]
  We propose a dual adversarial fine-tuning framework that jointly optimize visual and semantic supervision signals from two modalities.<n>Our approach outperforms the state-of-the-art method in adversarial robustness evaluation across zero-shot classification, image captioning, and visual question answering tasks.
  arXiv  Detail & Related papers  (2026-07-21T10:49:29Z)
• CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs [100.38986535324284]
  We introduce textbfCIAware-Bench, a benchmark for measuring textbfcontrol textbfintervention (CI) awareness across frontier models.<n>CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention.
  arXiv  Detail & Related papers  (2026-06-09T16:24:16Z)
• Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
  Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
  arXiv  Detail & Related papers  (2026-05-20T22:17:29Z)
• Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models [2.1198879079315573]
  On-device Vision-Language Models (VLMs) promise data privacy via local execution.<n>We show that the architectural shift toward Dynamic High-Resolution preprocessing introduces an inherent algorithmic side-channel.<n>We demonstrate a dual-layer attack framework against local VLMs.
  arXiv  Detail & Related papers  (2026-03-26T12:53:49Z)
• The Trojan in the Vocabulary: Stealthy Sabotage of LLM Composition [31.827344197678126]
  Tokenizer transplant introduces a supply-chain vulnerability.<n>By exploiting the geometry of coefficient reuse, our attack creates an asymmetric realizability gap.<n> Empirically, the attack is training-free and achieves spectral mimicry to evade outlier detection.
  arXiv  Detail & Related papers  (2025-12-31T19:00:03Z)
• When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models [81.7618160628979]
  Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored.<n>We introduce UPA-RFAS (Universal Patch Attack via Robust Feature, Attention, and Semantics), a unified framework that learns a single physical patch in a shared feature space.<n> Experiments across diverse VLA models, manipulation suites, and physical executions show that UPA-RFAS consistently transfers across models, tasks, and viewpoints.
  arXiv  Detail & Related papers  (2025-11-26T09:16:32Z)
• TRAP: Targeted Redirecting of Agentic Preferences [3.6293956720749425]
  We introduce TRAP, a generative adversarial framework that manipulates the agent's decision-making using diffusion-based semantic injections.<n>Our method combines negative prompt-based degradation with positive semantic optimization, guided by a Siamese semantic network and layout-aware spatial masking.<n>TRAP achieves a 100% attack success rate on leading models, including LLaVA-34B, Gemma3, and Mistral-3.1.
  arXiv  Detail & Related papers  (2025-05-29T14:57:16Z)
• CeTAD: Towards Certified Toxicity-Aware Distance in Vision Language Models [16.5022773312661]
  We propose a universal certified defence framework to safeguard large vision-language models against jailbreak attacks.<n>First, we proposed a novel distance metric to quantify semantic discrepancies between malicious and intended responses.<n>Then, we devise a regressed certification approach that employs randomized smoothing to provide formal robustness guarantees.
  arXiv  Detail & Related papers  (2025-03-08T17:33:55Z)
• Transferable Adversarial Attacks on SAM and Its Downstream Models [87.23908485521439]
  This paper explores the feasibility of adversarial attacking various downstream models fine-tuned from the segment anything model (SAM)<n>To enhance the effectiveness of the adversarial attack towards models fine-tuned on unknown datasets, we propose a universal meta-initialization (UMI) algorithm.
  arXiv  Detail & Related papers  (2024-10-26T15:04:04Z)
• 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.