Oversight mechanisms, approval workflows, and escalation patterns
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Search insteadXiaochen Zheng, Zhiwen Jiang, Melanie Guerard +2
Target Safety Assessment (TSA) requires systematic integration of heterogeneous evidence, including genetic, transcriptomic, target homology, pharmacological, and clinical data, to evaluate potential safety liabilities of therapeutic targets. This process is inherently iterative…
Thanh Luong Tuan, Abhijit Sanyal
Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer…
Srimonti Dutta, Akshata Kishore Moharir
Agentic AI systems retrieve private context, invoke tools, write files, call external services, coordinate with other agents, and may act without human approval. Existing bill of materials artifacts improve transparency for dependencies, model metadata, and training provenance,…
Mohammadreza Rashidi
AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe. That literature is scattered. Papers on sandbox isolation, capability…
Kiarash Ahi, Vaibhav Agrawal, Saeed Valizadeh
As AI shifts from human-in-the-loop interfaces to autonomous multi-agent systems capable of real-time code execution and tool integration through protocols like the Model Context Protocol (MCP), traditional SAST, DAST, and legacy AI safety methods fail to detect modern…
M. Llambí-Morillas, D. Fernández-Fernández
Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority, but do not inherently provide cryptographic evidence…
Arslan Brömme
Autonomous AI agents increasingly communicate with other agents, invoke tools, exchange intermediate results, and request human approvals. These workflows create a new auditability problem: organizations must reconstruct what happened, when it happened, which agent or human was…
Dingjie Song, Hanrong Zhang, Dawei Liu +10
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely…
Anees Ahmed Mahaboob Ali, R. Delhibabu, Everette Jacob Remington Nelson
Background. Artificial-intelligence design tools now plan genome-scale edits, and agentic systems execute those plans with progressively less human oversight. Biosecurity controls are limited to two points: refusal guardrails at the foundation model and sequence-identity…
Jia-Chen Zhang, Zenghui Zhang, Kai-Wei Zhang
Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to indirect prompt injection attacks. A widely adopted defense is the human-in-the-loop paradigm, in which the agent pauses for…
Tongxin Yuan, Zhiwei He, Lingzhong Dong +9
Introduces R-Judge benchmark for evaluating whether LLM agents can identify safety risks in agentic scenarios involving tool use and multi-step reasoning.