arXiv AI
Jul 13
The Patchwork Problem in LLM-Generated Code
arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.
Description-Code Inconsistency in Real-world MCP Servers: Measurement, Detection, and Security Implications
arXiv:2606. 04769v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as a critical standard empowering Large Language Models (LLMs) to utilize external tools.
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arXiv Machine Learning
4d ago
MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers
arXiv:2508.14925v2 Announce Type: replace-cross Abstract: By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a...
arXiv AI
Sep 15
Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
arXiv:2603.18740v3 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...
arXiv AI
Jul 21
Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.
arXiv AI
Jun 6
A Taxonomy of Runtime Faults in Model Context Protocol Servers
arXiv:2606. 05339v1 Announce Type: cross Abstract: MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol.
arXiv AI
Jul 15
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
arXiv:2607. 12723v1 Announce Type: cross Abstract: Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities.
arXiv AI
Aug 24
AEGIS: Preventing Cross-Domain Resource Abuse in MCP
AEGIS is a policy enforcement component designed to prevent resource abuse in the Model Context Protocol (MCP), an open‑source JSON‑RPC protocol that allows large language models to interact with external systems via tools. By leveraging large language models to analyze, categorize, and normalize diverse tool invocations across text, images, video, and location modalities, AEGIS provides administrators with fine‑grained safeguards that can be enforced through the Open Policy Agent and ContextForge AI Gateway. This approach enables detection and mitigation of abusive behaviors—such as excessively large search requests or long video calls—while maintaining the flexibility of MCP‑based agent ecosystems.
arXiv AI
Jul 15
Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs
arXiv:2607. 12273v1 Announce Type: cross Abstract: As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences.
arXiv AI
Jul 2
Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces
arXiv:2607. 00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs).
arXiv AI
Sep 12
No-Box Vulnerability Analysis: Description-only Detection of Indirect Prompt Injection Vulnerabilities in MCP Servers
The paper introduces no‑box vulnerability analysis, a method that detects security flaws without system access or runtime interaction by examining only the functionality metadata of a target. Using this approach, the authors built MCPSEC to audit Model Context Protocol servers for indirect prompt injection vulnerabilities, evaluating it on 20 servers with 177 tools. MCPSEC identified 143 vulnerable tools, achieving 98.9% recall of verified vulnerabilities, outperforming an LLM baseline.
arXiv AI
Jul 1
A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
arXiv:2606. 31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators.
arXiv AI
Jul 8
Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
arXiv:2607. 05744v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is the dominant way coding agents discover and invoke external tools.