Securing Multimodal AI through Internal Information Decomposition
Multimodal large language models (MLLMs) are vulnerable to cross-modal adversarial attacks that evade unimodal safeguards. The paper proposes FlowGuard, a lightweight inference-time defense that detects harmful inputs by monitoring internal cross-modal consistency using FlowVectors inspired by Partial Information Decomposition. FlowVectors quantify cross-modal redundancy, synergy, and modality-specific dominance to identify abnormal fusion behavior caused by adversarial manipulation. FlowGuard r
Analysis
TL;DR
- Multimodal large language models (MLLMs) are vulnerable to cross-modal adversarial attacks that evade unimodal safeguards.
- The paper proposes FlowGuard, a lightweight inference-time defense that detects harmful inputs by monitoring internal cross-modal consistency using FlowVectors inspired by Partial Information Decomposition.
- FlowVectors quantify cross-modal redundancy, synergy, and modality-specific dominance to identify abnormal fusion behavior caused by adversarial manipulation.
- FlowGuard reduces attack success rates from >90% to <15% on unseen attacks with minimal utility loss (<3%) and up to 6x latency reduction compared to existing methods.
- The approach trains solely on benign data in a one-class classification setting, making it practical for deployment without requiring adversarial examples.
Why It Matters
This work addresses a critical security gap in multimodal AI systems where traditional unimodal defenses fail against distributed adversarial attacks across modalities. By shifting focus from raw input/output inspection to internal fusion consistency, FlowGuard provides a computationally efficient defense that preserves model utility while significantly improving robustness against sophisticated multimodal threats. The approach offers a new paradigm for securing MLLMs that could become standard practice as these models deploy in high-stakes applications.
Technical Details
- FlowGuard leverages Partial Information Decomposition (PID) concepts to create FlowVectors that decompose multimodal information into redundancy (shared information across modalities), synergy (information only present when modalities are combined), and modality-specific dominance (information unique to one modality).
- The framework operates at inference time by monitoring the consistency between unimodal reasoning (text-only and vision-only predictions) and the final fused multimodal prediction, detecting anomalies caused by adversarial manipulation.
- Training requires only benign data in a one-class classification setup, eliminating the need for adversarial examples during defense training.
- The method achieves up to 6x latency reduction compared to existing defense approaches while maintaining <3% utility loss on clean inputs.
- Evaluation demonstrates effectiveness against unseen attacks, reducing attack success rates from over 90% to below 15% across various multimodal attack scenarios.
Industry Insight
- Security teams deploying multimodal AI systems should prioritize internal consistency monitoring over traditional input/output inspection methods to defend against sophisticated cross-modal attacks.
- The one-class training approach of FlowGuard offers practical deployment advantages since organizations can implement defenses without collecting or generating adversarial examples.
- As multimodal models become more prevalent in critical applications, this consistency-based defense paradigm may become a standard component of secure MLLM architectures, potentially influencing future model design to inherently support such monitoring capabilities.
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