The Model Context Protocol (MCP) has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical. In practice, due to the absence of large-scale runtime MCP servers, such understanding largely relies on security scanners applied to a small number of cases, yet the reliability of these assessments remains unclear. In this study, we revisit how MCP security is measured. We present MCPZoo, the largest collection of MCP servers for dynamic analysis to date. MCPZoo is constructed through a multi-agent framework for transforming in-the-wild static repositories into dynamic services. The framework emulates how human experts build, diagnose, and iteratively repair deployment and runtime defects by combining environment inference with feedback-driven refinement. To ensure practical interactivity at runtime, the servers are validated via real protocol interactions. As a result, MCPZoo contains 64,611 unique MCP servers (113,927 in total), with more than 37,288 supporting dynamic analysis. Leveraging MCPZoo, we conduct the first ecosystem-scale measurement of MCP servers and the scanners that analyze them. While existing scanners report that 96.89% of servers are risky, we find that these signals are unreliable. In particular, manual validation shows that less than 50% of sampled alerts are true positives, and scanner outputs exhibit clear inconsistency across scanners. Overall, MCPZoo enables large-scale, reproducible measurement of MCP server security and exposes limitations of current scanning practices. We further release a public query interface to support practical risk assessment of MCP servers.
Pei Chen, Baichao An, Mengying Wu et al.· 0 citations
This work proposes ActivationBackdoor, an inference-time backdoor attack that composes two activation-level components for trigger detection and backdoor behavior injection, and shows that ActivationBackdoor attains attack success comparable to training-time backdoor baselines while preserving high clean-task accuracy and utility.
Zichun Su, Mi Zhang, Xiaohan Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Text-to-image diffusion models have achieved high visual fidelity and broad adoption, but remain vulnerable to safety violations when adversaries exploit them to synthesize illicit content. Existing alignment paradigms, from input sanitization to structural feature pruning, are largely organized around unsafe concepts explicitly exposed during filtering, editing, or localization. This leaves a blind spot for visual synonym attacks (VSA), a jailbreak where benign-looking prompts elicit prohibited imagery through implicit visual associations. As a result, current defenses face a safety-utility dilemma: they may either under-mitigate VSA threats or over-suppress visually similar benign concepts. The core challenge is that VSA hides the unsafe target at the textual surface while revealing it through generation-time visual-semantic convergence. In this work, we therefore shift from static suppression of pre-specified unsafe concepts to dynamic tracing of how unsafe semantics emerge during generation. Our mechanistic analysis shows that VSA and explicit unsafe prompts converge through sparse semantic-injecting attention heads, which serve as inference-time bottlenecks for prohibited visual semantics. Based on this insight, we propose AEGIS (Adaptive Evasion Guard via Identification and Steering), an inference-time defense that applies similarity-aware repulsion only at the identified vulnerable heads. Evaluated against 16 baselines, AEGIS improves both safety and utility. On SD 1.4, it reduces ASR to $\mathbf{0.00}/\mathbf{0.03}$ for in-domain violence/nudity VSA and achieves ASRs $\le \mathbf{0.09}$ on out-of-domain explicit and adversarial attacks. It preserves benign fidelity, avoids suppressing hard-negative concepts, and transfers to SD 2.1 and FLUX.1 after re-identifying the critical heads for each backbone.
Yuanmin Huang, Zhenfei Zhang, Mi Zhang et al.· 0 citations
Collaborative inference enables cost-effective deployment of large language models by partitioning layers across multiple participants and forwarding intermediate activations between participants in a pipeline, but these transmitted activations also create a new attack surface: a malicious participant can manipulate intermediate activations during inference. Prior work on collaborative inference attacks has largely focused on privacy leakage, leaving the backdoor threat insufficiently explored. Inspired by recent advances in representation engineering, we propose ActivationBackdoor, an inference-time backdoor attack that composes two activation-level components for trigger detection and backdoor behavior injection. This design achieves the same ''clean inputs behave normally, triggered inputs induce attacker-specified behavior'' property as traditional backdoor attacks, while requiring no access to training data and no model parameter updates. Experiments across classification and open-ended generation tasks show that ActivationBackdoor attains attack success comparable to training-time backdoor baselines while preserving high clean-task accuracy and utility. Overall, our results expose a new and practical backdoor risk in collaborative inference arising from intermediate activation exposure.
Zichun Su, Mi Zhang, Xiaohan Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Results show that evidence-grounded detection can assess both execution-related and semantic risks in MCP interactions, and compared with existing dynamic scanners, FlowGuard reduces end-to-end latency by up to 2.23x.
Baichao An, Pei Chen, Geng Hong et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
