1
2
3
Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents
多用户LLM Agent的安全治理框架Harness-MU，保障可控与高效。
arXiv:2606.21856v1 Announce Type: cross Abstract: The increasing deployment of large language model (LLM) agents in collaborative workflows demands ro…
4
A Layered Security Framework Against Prompt Injection in RAG-Based Chatbots
了解RAG聊天机器人防注入攻击的分层安全框架，开放获取的学术前沿。
arXiv:2606.19660v1 Announce Type: cross Abstract: Prompt injection is ranked as the most critical vulnerability in large language model (LLM) deployme…
5
Automated Framework to Evaluate and Harden LLM System Instructions against Encoding Attacks
提出自动化框架，系统评估并加固LLM系统指令以抵御编码攻击，为AI安全提供新工具。
arXiv:2604.01039v2 Announce Type: replace-cross Abstract: System Instructions in Large Language Models (LLMs) are commonly used to enforce safety poli…
6
Auditing Agent Harness Safety
这篇论文系统审计了智能体框架的安全隐患，为构建可信AI系统提供关键方法论。
arXiv:2605.14271v2 Announce Type: replace Abstract: LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, an…
7
Operator System Card
OpenAI 发布 Operator 系统卡，详解多层级安全防护与红队测试成果。
Drawing from OpenAI’s established safety frameworks, this document highlights our multi-layered approach, including model and product mitigations we’v…
8
Working with US CAISI and UK AISI to build more secure AI systems
OpenAI与美英AI安全机构合作，共建更安全的AI系统，最新进展一览。
OpenAI shares progress on the partnership with the US CAISI and UK AISI to strengthen AI safety and security.