arXiv Machine Learning
Jun 25
ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures
arXiv:2606. 19380v3 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents.
AgentArmor: A Framework, Evaluation, \& Mitigation of Coding Agent Failures
arXiv:2606. 19380v1 Announce Type: cross Abstract: Software engineering and deployment are increasingly being delegated to AI coding agents.
Related stories
arXiv AI
Aug 3
Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
arXiv:2607. 29254v1 Announce Type: new Abstract: AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions.
arXiv AI
Jul 28
Execution-Grounded Security Testing for Coding Agents in Software Engineering Pipelines
arXiv:2607. 22569v1 Announce Type: new Abstract: Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, and the underlying system.
arXiv AI
Jul 29
Distributing Security Controls Through Harness Engineering
arXiv:2607. 25890v1 Announce Type: new Abstract: AI coding agents are being adopted at historic speed, yet security and risk concerns remain the primary barrier to scaling agentic AI across organizations.
arXiv AI
Jul 13
Failure as a Process: An Anatomy of CLI Coding Agent Trajectories
arXiv:2607. 09510v1 Announce Type: cross Abstract: Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern.
arXiv AI
Jun 18
Code-Augur: Agentic Vulnerability Detection via Specification Inference
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
arXiv AI
Jul 24
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
arXiv:2607. 20759v1 Announce Type: cross Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools.
arXiv AI
Jul 22
Data Leakage Prevention in Agentic Applications via Preemptive Hardening
arXiv:2607. 18847v1 Announce Type: cross Abstract: Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks.
arXiv AI
Jul 16
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.
arXiv AI
Aug 5
Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
arXiv AI
Jul 7
Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents
arXiv:2604. 15579v2 Announce Type: replace-cross Abstract: There is increasing interest in integrating AI agents that invoke tools into domain-specific commercial software, where unintended tool calls can cause serious security and safety incidents.
arXiv AI
Jul 3
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
arXiv:2607. 01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks.