Run Hao

Zonghao Ying, Siyang Wu, Run Hao et al.

Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and improve their safety, we organized the Adversarial Testing & Large-model Alignment Safety Grand Challenge (ATLAS) 2025}. This technical report presents findings from the competition, which involved 86 teams testing MLLM vulnerabilities via adversarial image-text attacks in two phases: white-box and black-box evaluations. The competition results highlight ongoing challenges in securing MLLMs and provide valuable guidance for developing stronger defense mechanisms. The challenge establishes new benchmarks for MLLM safety evaluation and lays groundwork for advancing safer multimodal AI systems. The code and data for this challenge are openly available at https://github.com/NY1024/ATLAS_Challenge_2025.

Run Hao, Peng Ying

The rise of text-to-image (T2I) models has enabled the synthesis of photorealistic human portraits, raising serious concerns about identity misuse and the robustness of AIGC detectors. In this work, we propose an automated adversarial prompt generation framework that leverages a grammar tree structure and a variant of the Monte Carlo tree search algorithm to systematically explore the semantic prompt space. Our method generates diverse, controllable prompts that consistently evade both open-source and commercial AIGC detectors. Extensive experiments across multiple T2I models validate its effectiveness, and the approach ranked first in a real-world adversarial AIGC detection competition. Beyond attack scenarios, our method can also be used to construct high-quality adversarial datasets, providing valuable resources for training and evaluating more robust AIGC detection and defense systems.

Run Hao, Zhuoran Tan

Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem expand risks across tool metadata, untrusted outputs, cross-tool flows, multimodal inputs, and supply-chain vectors. Existing MCP benchmarks largely measure robustness to malicious inputs but offer limited remediation guidance. We present MCP Pitfall Lab, a protocol-aware security testing framework that operationalizes developer pitfalls as reproducible scenarios and validates outcomes with MCP traces and objective validators (rather than agent self-report). We instantiate three workflow challenges (email, document, crypto) with six server variants (baseline and hardened) and model three attack families: tool-metadata poisoning, puppet servers, and multimodal image-to-tool chains, in a unified, trace-grounded evaluation. In Tier-1 static analysis over six variants (36 binary labels), our analyzer achieves F1 = 1.0 on four statically checkable pitfall classes (P1, P2, P5, P6) and flags cross-tool forwarding and image-to-tool leakage (P3, P4) as trace/dataflow-dependent. Applying recommended hardening eliminates all Tier-1 findings (29 to 0) and reduces the framework risk score (10.0 to 0.0) at a mean cost of 27 lines of code (LOC). Finally, in a preliminary 19-run corpus from the email system challenge (tool poisoning and puppet attacks), agent narratives diverge from trace evidence in 63.2% of runs and 100% of sink-action runs, motivating trace-based auditing and regression testing. Overall, Pitfall Lab enables practical, end-to-end assessment and hardening of MCP tool servers under realistic multi-vector conditions.