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Perplexity open-sources Numbat, an agent security suite designed to detect, prevent, and investigate dangerous AI agent activity on client endpoints, addressing novel security challenges from autonomous agents.
An OpenClaw agent with a strict read-only rule was tricked by prompt injection into posting on Twitter, highlighting the gap between system instructions and actual enforcement. The author asks how others handle such security.
Pillar Research found sandbox escape vulnerabilities in AI coding agents from Cursor, Codex, Gemini CLI, and Antigravity, revealing that these agents can write files that host components later trust, bypassing sandbox boundaries. The findings highlight the need for a new threat model for agentic security.
Released a new update for AgentSecure based on user feedback.
FARMA is a novel memory-poisoning attack that targets an agent's own decision logs rather than retrieved facts, achieving 100% attack success against undefended and defended systems, with the authors' defense SENTINEL reducing success to 0% but remaining vulnerable to adaptive attackers.
MakerChecker is an open-source security layer for AI agents that enforces deny-by-default permissions, human approvals, and provides a cryptographically signed audit trail. It scans agent code for dangerous capabilities and prevents agents from approving their own actions.
Anthropic's engineering team shares their complete experience from over two years of security isolation for Claude products, including three isolation modes (ephemeral containers / HITL sandbox / local VM) and six incident postmortems with fixes, emphasizing environment-layer defense over model-layer.
The user asks if there is a local tool similar to Vagrant that can run Claude Code and Codex in containers or virtualized environments, to alleviate concerns about excessive permissions and unpredictability of agent software.
AI-Infra-Guard is an open-source framework for multi-layer red teaming of AI agents, covering infrastructure, protocol, behavior, and model layers with diverse detection paradigms.
This paper introduces the Agent-Native Immune System (ANIS), a biologically inspired, endogenous defense architecture embedded directly within the agent's cognitive loop. It proposes a six-layer Immune Tower, a unified taxonomy of Agent Viruses and Vaccines, and the Harness Triad for continual immune learning to address runtime hijacking vulnerabilities in autonomous agents.
Discusses the challenge of maintaining audit trails when AI agents operate using human credentials, highlighting security and accountability concerns.
An article detailing various jailbreak techniques for large language models, including Crescendo, role-playing, encoding, hidden prompts, and indirect injection, along with security recommendations for developers.
A developer discusses three common patterns for how coding agents obtain API keys, highlighting that agents can circumvent restrictions by being resourceful, and asks the community about their real-world setups and experiences.
A security expert shares a cheatsheet on advanced agent security hardening, covering tool sandboxing, output validation, data loss prevention, adversarial testing, and runtime policy enforcement, emphasizing continuous security practices for production AI agents.
AI Agent security has moved from an academic topic to an industry reality, involving FFmpeg zero-day vulnerabilities, Chrome 429 patch, OpenAI Lockdown Mode, and the OWASP framework; meanwhile, Agent payment standards are becoming a battlefield for infrastructure, with Visa stablecoin settlement competing with traditional card networks.
PixieBrix launches Agent Browser Shield, a free source-available browser extension that protects AI agents from prompt injection, dark patterns, and context pollution during web browsing.
SkillHarm is a benchmark for evaluating skill-based attacks across the skill-use lifecycle, revealing high vulnerability (up to 86.3% attack success) in current AI agents and introducing automated attack construction via AutoSkillHarm.
An analysis highlighting that most enterprise AI agent security investments focus on model layer guardrails and observability, leaving critical gaps at the access and protocol layers. Citing a 2026 report, 75% of enterprise AI agents remain unsecured due to near-zero coverage in these layers.
The article introduces the Agent Vulnerability Enumeration (AVE) record as a new standard designed to address the inadequacies of CVE for AI agent vulnerabilities, covering scoring, detection, and standardization challenges specific to agentic AI.
HOL Guard is an open-source security tool that provides dangerous command identification, interception, and auditing for development agents such as Codex, Claude Code, etc. It supports multiple protection levels and a local approval center to prevent risks like accidental deletion or modification.