GLST: Defending Confidence-Driven V2X Collaborative Perception Against Stealthy Multi-Attacker Feature Injection

• URL: http://arxiv.org/abs/2607.23059v1
• Date: Sat, 25 Jul 2026 06:05:35 GMT
• Title: GLST: Defending Confidence-Driven V2X Collaborative Perception Against Stealthy Multi-Attacker Feature Injection
• Abstract summary: Collaborative perception improves autonomous-driving perception by enabling connected vehicles to exchange intermediate features via V2X.<n> Confidence-driven sparse communication reduces bandwidth by transmitting only perception-critical spatial regions.<n>Once a collaborator is compromised, malicious features in high-confidence or ego-uncertain regions may be preferentially selected and amplified during fusion.<n>We propose Global-Local Structural Trust (GLST), a lightweight defense that assesses collaborator reliability through three complementary perspectives.
• Score: 18.564330660698676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative perception (CP) improves autonomous-driving perception by enabling connected vehicles to exchange intermediate features via V2X. Confidence-driven sparse communication reduces bandwidth by transmitting only perception-critical spatial regions, but creates a security risk: once a collaborator is compromised, malicious features in high-confidence or ego-uncertain regions may be preferentially selected and amplified during fusion. Using Where2comm as a representative framework, we show that the proposed Pretend Benign attack exploits its spatial-confidence mechanism by injecting stealthy perturbations into uncertain yet perception-critical regions, substantially degrading 3D object detection while preserving benign-like feature characteristics. Beyond this attack-framework pair, we identify a broader weakness of existing trust-based defenses: their reliance primarily on a single consistency signal leaves them vulnerable when multiple attackers form a pseudo-consensus that biases trust estimation. We therefore propose Global-Local Structural Trust (GLST), a lightweight defense that assesses collaborator reliability through three complementary perspectives: global feature consistency, multi-scale local residual consistency, and structural consistency with ego-side semantic topology. The resulting trust scores guide feature fusion to suppress unreliable collaborators. Experiments on OPV2V show that GLST achieves competitive performance against single-attacker Pretend Benign attacks and substantially stronger robustness in multi-attacker settings. Under a four-attacker Pretend Benign attack, GLST maintains 0.69 AP@0.3, whereas existing single-signal defenses degrade severely. GLST also remains effective against gradient-based attacks such as PGD, indicating that multi-level trust modeling is essential for securing confidence-driven CP.

Related papers

• Shared Vulnerabilities in Robustness-Optimized Defenses: One Breach Exposes the Family [22.335085458688372]
  Adversarial robustness optimization aims to preserve correct prediction under adversarial perturbations.<n>We identify a new security risk: these gains can create shared vulnerabilities across defenses.<n>We introduce stricter transfer-only protocols and a deliberately simple adaptive attack, PGDTransfer, to test whether robustness-optimized defenses share transfer-only vulnerability.
  arXiv  Detail & Related papers  (2026-07-20T01:17:28Z)
• 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)
• TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning [60.68349524623048]
  Decompositional jailbreaks pose a critical threat to large language models.<n>We present TwinGate, a stateful dual-encoder defense framework.<n>We construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents.
  arXiv  Detail & Related papers  (2026-04-30T13:44:01Z)
• TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models [19.148124494194317]
  We propose TraceGuard, a process-guided security framework that transforms small-scale models into robust reasoning firewalls.<n>Our approach treats the reasoning trace as an untrusted payload and establishes a defense-in-depth strategy.<n>We demonstrate robustness against adaptive adversaries in a grey-box setting, establishing TraceGuard as a viable, low-latency security primitive.
  arXiv  Detail & Related papers  (2026-03-02T22:19:13Z)
• Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation [5.905949608791961]
  This paper introduces a semantics-driven federated IDS framework that incorporates language-derived semantic supervision into federated optimization.<n>The framework achieves over 80% zero-shot detection accuracy on unseen attack patterns, improving zero-day discrimination by more than 10% compared to similarity-based baselines.
  arXiv  Detail & Related papers  (2026-01-30T16:38:05Z)
• Debiased Dual-Invariant Defense for Adversarially Robust Person Re-Identification [52.63017280231648]
  Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking.<n>Person ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions.<n>We propose a dual-invariant defense framework composed of two main phases.
  arXiv  Detail & Related papers  (2025-11-13T03:56:40Z)
• GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks [56.983319121358555]
  Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors.<n>We introduce the Dual-Facet Attack (DFA), a novel threat model that concurrently undermines predictive accuracy and group fairness.<n>We propose GuardFed, a self-adaptive defense framework that maintains a fairness-aware reference model using a small amount of clean server data.
  arXiv  Detail & Related papers  (2025-11-12T13:02:45Z)
• The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability [7.452202188661883]
  We introduce and empirically validate the Trust-Vulnerability Paradox (TVP)<n>TVP: increasing inter-agent trust to enhance coordination simultaneously expands risks of over-exposure and over-authorization.<n>This study formalizes TVP, establishes reproducible baselines with unified metrics, and demonstrates that trust must be modeled and scheduled as a first-class security variable in multi-agent system design.
  arXiv  Detail & Related papers  (2025-10-21T12:18:39Z)
• GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detection [11.336965062177722]
  Collaborative perception is vulnerable to adversarial message attacks from malicious agents.<n>This paper reveals a novel blind area confusion (BAC) attack that compromises existing single-shot outlier-based detection methods.<n>We propose Guarded Collaborative Perception framework based on spatial-temporal aware malicious agent detection.
  arXiv  Detail & Related papers  (2025-01-05T06:03:26Z)
• Adversarial Robustness under Long-Tailed Distribution [93.50792075460336]
  Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. In this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. We propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant and data re-balancing.
  arXiv  Detail & Related papers  (2021-04-06T17:53:08Z)
• 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.