airflowattack-7da29c10·1 events·first seen Aliases: AirflowAttack
Researchers introduce AirflowAttack, the first adversarial attack targeting vision-language models deployed on infrared remote-sensing imagery, using physically plausible thermal-airflow turbulence as the perturbation prior. A single input-agnostic perturbation optimized on one surrogate CLIP model achieves 48.5% mean zero-shot attack success rate across five CLIP backbones, outperforming four IR-specific physical baselines. Applied to six state-of-the-art VLMs, the attack reduces scene-classification accuracy by up to 38.2% relative while paradoxically increasing model confidence, causing confabulation of thermal artifacts as genuine evidence. The work also releases a benchmark spanning eleven models and four tasks, exposing systematic vulnerabilities in security-critical IR VLM deployments.