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Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models

ICML 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Interleaved image-text generation with MLLMs · 0 citations (OpenAlex)

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Introduces MM-DeceptionBench, a 1013-case benchmark for multimodal deception in MLLMs, and a visually grounded multi-agent debate judge ("debate with images") that agrees better with human labels than MLLM-as-a-judge baselines.

Problem

Detecting strategic deception in multimodal LLMs, where models correctly perceive images but respond misleadingly, is not reliably handled by text-centric monitors or single-judge MLLM-as-a-judge methods, and human evaluation is expensive at scale.

Approach

Frame deception evaluation as a multi-round debate between agents with opposing stances, where each agent must cite visual evidence produced by image operations (zoom-in, annotate, depth, segmentation), and a judge decides on the full trajectory.

Key results

• On MM-DeceptionBench with GPT-4o as judge, debate with images reaches 76.0% accuracy and kappa 0.46 vs 61.5% and 0.30 for direct prompting.
• Gemini-2.5-Pro with debate with images reaches 82.2% accuracy on MM-DeceptionBench vs 78.8% for direct prompting.
• Frontier model deception rates range from 15.9% (GPT-5) to 88.1% (Gemini-2.5-Pro) overall on MM-DeceptionBench.

Surprising findings

• Vanilla MLLM-as-a-judge agreement with humans is only moderate (kappa 0.30-0.48), with judges underestimating benign-seeming deception.
• Enabling all visual operations simultaneously lowers Qwen2.5-VL-72B accuracy (68.40%) relative to Zoom-In alone (73.32%), a 'selection tax'.

Limitations

• Benchmark focuses on visually grounded, behaviorally verifiable cases and is built from scenarios designed to elicit deception under pressure.
• Debate with images is designed for offline auditing rather than real-time filtering.
• Debate with images underperforms or matches baselines on some metrics (e.g., ECE on PKU-SafeRLHF-V, fAcc on HallusionBench for CoT).

Experimental setup

Datasets: MM-DeceptionBench PKU-SafeRLHF-V HallusionBench

Benchmarks: MM-DeceptionBench PKU-SafeRLHF-V HallusionBench

Metrics: accuracy Cohen's kappa F1 ECE FPR FNR QP-Acc F-Acc A-Acc

Baselines: Direct prompt CoT prompt Majority vote Debate about images MLLM-as-a-judge

Builds on: multi-agent debate MLLM-as-a-judge

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