AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models
arXiv:2607.06485v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial robustness remains unexamined. We present AirflowAttack, to our knowledge the first adversarial attack for IR remote-sensing VLMs and the first to weaponize thermal-airflow turbulence as the perturbation prior. A lightweight generator synthesizes a single input-agnostic perturbation regularized toward physically plausible airflow patterns. Optimized on one surrogate CLIP model, it attains a mean ze
The increasing deployment of VLMs in security-critical infrared remote sensing makes understanding their vulnerabilities urgent as these systems become operational.
This research reveals a novel attack vector against critical vision-language models used in defense and security, highlighting a significant cybersecurity risk for advanced AI deployments.
The understanding of AI model robustness extends to infrared vision systems, introducing a new threat model based on physically plausible environmental perturbations.
- · Cybersecurity researchers
- · Adversarial AI developers
- · Defense contractors focused on AI security
- · Security-critical VLM deployers
- · Infrared remote sensing operators
- · AI systems lacking adversarial robustness
Immediate efforts will be made to patch or redesign infrared VLM systems to counter thermal-airflow attacks.
An increase in investment and research into 'physical-world' adversarial AI defenses and robust VLM architectures will likely follow.
This could lead to a 'cyber-physical' arms race where environmental manipulation becomes a standard tactic for disrupting autonomous systems.
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Read at arXiv cs.AI