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Test-Time Backdoor Attacks on Multimodal Large Language Models
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Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase. In this work, we present AnyDoor, a test-time backdoor attack against multimodal large language models (MLLMs), which involves injecting the backdoor into the textual modality using adversarial test images (sharing the same universal perturbation), without requiring access to or modification of the training data. AnyDoor employs similar techniques used in universal adversarial attacks, but distinguishes itself by its ability to decouple the timing of setup and activation of harmful effects. In our experiments, we validate the effectiveness of AnyDoor against popular MLLMs such as LLaVA-1.5, MiniGPT-4, InstructBLIP, and BLIP-2, as well as provide comprehensive ablation studies. Notably, because the backdoor is injected by a universal perturbation, AnyDoor can dynamically change its backdoor trigger prompts/harmful effects, exposing a new challenge for defending against backdoor attacks. Our project page is available at https://sail-sg.github.io/AnyDoor/.
Forward citations
Cited by 6 Pith papers
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Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation
BadSem shows that semantic mismatches between images and text can serve as stealthy backdoor triggers for VLMs, achieving near-perfect attack success with low poisoning rates.
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TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
TokenSwap poisons LVLMs so that triggered images produce captions with subject and object roles reversed, achieving high attack success while evading a perplexity-based detector.
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Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation
A single universal adversarial image perturbation can route different input semantics to different attacker-defined outputs in multimodal LLMs, with up to 66% success over five targets.
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A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
A survey of LVLM safety that adds a lifecycle taxonomy and new benchmark results showing Janus-Pro-7B has weaker safety than several open-source LVLMs.
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Backdoor Attacks and Defenses in Computer Vision Domain: A Survey
A literature review that classifies backdoor attacks and defenses in computer vision into a five-axis taxonomy and identifies supply-chain, hardware, and cross-task evaluation as open gaps.
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