All news with #agent security tag

🛡️ Recent developments show AI moving from automation to autonomous cyber operations, shifting how offense and defense interact. The Anthropic Mythos Preview and related incidents illustrate models discovering and chaining vulnerabilities with limited human direction, prompting coordinated defensive responses from major vendors. Policy and procurement are adapting, and security leaders must treat AI agents as principals, invest in adaptive defenses, and reframe risk models for continuous compromise.

🚦The author argues that traditional post-hoc compliance reviews fail for AI because AI systems change continuously. Drawing on research into Chinese and EU approaches, the piece recommends embedding governance into CI/CD pipelines so model cards, data lineage and risk evaluations are generated and enforced as deployment gates. It also urges treating agent identity as first-class security control and positioning compliance as operational release infrastructure rather than a review layer.

🛡️ Researchers argue enterprises must stop treating AI agents as trusted components and instead secure them as untrusted systems. The paper, authored by teams from Google, UC San Diego, UW–Madison and others, distills five systems-security principles—least privilege, tamper resistance, complete mediation, secure information flow, and human risk—and maps eleven real-world agent attacks to these violations. They caution that stacking ML guardrails is insufficient and propose research directions for separating instructions from data, verifiable least-privilege policies, and information-flow controls.

🔒 Google is folding CodeMender into its broader Agent Platform strategy, expanding the AI-powered security agent from standalone vulnerability remediation toward an integrated, governed enterprise agent ecosystem. Launched in October 2025 to autonomously identify and patch vulnerabilities using Gemini models, CodeMender reportedly upstreamed dozens of fixes but lacks published performance metrics on accuracy and regressions. The integration emphasizes governance, observability, and identity, positioning CodeMender as a controlled participant in AI-native development and security pipelines rather than an unsupervised remediation tool.

🚀 At Google I/O ‘26, Google Cloud announced expanded agentic capabilities, new frontier models, and developer tools to help startups move from prototype to production. Highlights include Gemini 3.5 Flash and Gemini Omni for multimodal content, Antigravity 2.0 as an agent control plane with CLI/SDK and dynamic subagents, and Managed Agents to run agent workloads securely in Google Cloud. The releases aim to accelerate development, reduce infrastructure overhead, and provide enterprise-grade security.

🛡️ This week's ThreatsDay bulletin highlights a string of notable cybersecurity developments, from 47 zero-day exploits revealed at Pwn2Own Berlin 2026 to active Linux rootkit evolution. It summarizes warnings about agentic AI, targeted intrusions using AI agents, and advisories on token and dependency leaks. The report also covers nation-state tensions, ransomware activity, encrypted communications, and campaigns abusing identity recovery flows.

🔒 Microsoft has open-sourced two tools, Rampart and Clarity, intended to embed safety engineering into the AI agent development lifecycle rather than leaving it as a periodic checkpoint. Rampart converts red-team findings into structured, repeatable tests that can be automated in CI/CD pipelines and is built on top of PyRIT for continuous adversarial and benign scenario execution. Clarity targets an earlier phase, guiding engineers through structured conversations to clarify assumptions, expected behaviors, permissions and trust boundaries, storing outcomes as markdown in a .clarity-protocol/ directory for review. Both projects join Microsoft’s broader open-source agent governance stack to address risks such as prompt injection, unsafe tool use, privilege escalation, and unintended autonomous actions.

🛡️ Microsoft has released two open-source tools, RAMPART and Clarity, to help developers test and clarify AI agent safety early in the development lifecycle. RAMPART is a Pytest-native framework for writing and running adversarial and benign safety tests against agents, building on prior work such as PyRIT. It evaluates test outcomes via simple adapters that connect an agent to the suite, while Clarity acts as a structured thinking partner to surface assumptions, explore failure modes, and guide design decisions before coding begins.

🚀 Google Cloud announced general availability of GKE Agent Sandbox and introduced the open-source Agent Substrate. Agent Sandbox is a cloud-native execution environment designed for AI agents, offering pod snapshots to suspend idle workloads, an integrated warm pool for sub-second provisioning, gVisor and pluggable kernel isolation, and standby suspended VMs to reduce warm-pool cost. Agent Substrate aims to provide a minimal control plane and scheduler optimizations to support ultra-dense, low-latency agent workloads at scale.

🔧 Google today introduced Agent Executor, an open-source runtime standard for durable, resumable, and distributed agent execution. It offers event logging and snapshotting to enable durable execution, secure sandbox isolation to limit harm, and a single-writer architecture to maintain session consistency. Agent Executor also supports connection recovery so clients can reconnect to long-running workflows. The project is available in preview and pairs with Agent Substrate to improve Kubernetes-scale agent scheduling.

🔒 Microsoft has open-sourced two engineering tools—RAMPART and Clarity—to make agent safety a continuous part of development. RAMPART provides a pytest-style framework that brings red-team and adversarial tests into CI, evaluating tools invoked and side effects. Clarity is a structured design companion that captures problem statements, failure analyses, and decisions in a .clarity-protocol directory. Both aim to create living safety artifacts integrated into normal workflows.

🔧 At Google I/O, Google Cloud introduced a unified developer toolkit that brings Antigravity 2.0, the Gemini Enterprise Agent Platform, the Managed Agents API, and ADK 2.0 into a shared protocol layer to accelerate local development and secure cloud deployment. The post outlines a four-rung ladder from low-code Agent Studio to code-first ADK, all underpinned by the interoperable A2A protocol. New Antigravity desktop and CLI tools provide a consistent harness for coding agents, while Managed Agents offer agent-as-a-service with sandboxed execution. The platform emphasizes governance, skill reuse, evaluation tooling, and secure pipelines for production.

🔒 The UK’s National Cyber Security Centre (NCSC) has published new guidance for organisations considering the adoption of agentic AI, summarising a wider report produced with Five Eyes partners. It flags the heightened risk from agent autonomy and complexity, including excessive access, unpredictable behaviour and actions that can outpace human review. The NCSC advises incremental deployment with tightly bounded pilots, clear ownership, ongoing monitoring and meaningful human oversight, and points organisations to industry best practice such as ETSI EN 304 223.

🛡️ Microsoft Security explains how rising agentic autonomy reorients security from models to how agents are assembled, constrained, and governed inside applications. The post identifies amplified risks—agent hijacking, intent breaking, data leakage, supply chain compromise—and shows why the application layer is decisive because builders fully control permissions, tool access, and failure handling. It recommends concrete design patterns: agents as microservices, least permissions, deterministic human-in-the-loop, and distinct agent identity to limit blast radius and preserve auditability.

🔍 AI coding agents now operate across IDEs, terminals, and extension runtimes, so defenders must expand focus beyond source code to repository files, instruction and runtime settings, and third‑party extensions that shape agent behavior. VirusTotal Code Insight and agentic threat intelligence apply semantic analysis to detect malicious intent in syntactically valid artifacts and link findings to broader campaigns and supply‑chain risks. Examples—weaponized tasks.json, malicious Skill.md, redirected settings.json endpoints, and sabotaged extensions—illustrate how semantics can enable exfiltration, privilege escalation, and stealthy attacker control.

🔐 Agentic AI is already operating across enterprises, executing tasks and taking actions often without meaningful security involvement. Security teams must develop hands‑on fluency — build and test agents, understand integrations like the Model Context Protocol, and enforce scoped configurations — because policy alone won't close the gap. The piece distinguishes three agent classes (productivity, MCP‑connected vendor agents, and custom user agents) and emphasizes configuration, access scoping, and training such as SANS SEC545 to reduce exposure.

🔒 OTT Cybersecurity LLC — the team behind Lyrie.ai — announced acceptance into Anthropic’s Cyber Verification Program and the public release of the Agent Trust Protocol (ATP). ATP is an open cryptographic standard that enables real-time verification of an AI agent’s identity, authorized scope, attestation status, delegation, and revocation. The protocol is royalty-free, slated for IETF submission, and a reference implementation is published under an MIT license. Lyrie positions itself as the security layer for autonomous AI agents operating on the internet.

🔐 Google Cloud announced IAM enhancements at Next '26 to secure agentic AI with Agent Identity and an Agent Gateway. Agent Identity, built on the open SPIFFE standard, provides cryptographically verifiable, automatically provisioned identities and new OAuth/certificate tooling. The Agent Gateway, Identity‑Aware Proxy, and Context‑Aware Access extend Zero Trust to agent traffic, while policy updates, guardrails, and Model Armor add runtime defenses.

🔒 AWS has announced general availability of the AWS MCP Server, a managed endpoint that gives AI coding agents secure, auditable access to AWS services using the Model Context Protocol (MCP). The server is part of the Agent Toolkit for AWS and enforces IAM-based guardrails while emitting CloudWatch metrics and CloudTrail logs so teams keep visibility and control. It supports calling any AWS API through a single tool, sandboxed Python execution for multi-step tasks without filesystem or shell access, and a new agent skills format for on-demand, curated procedures. The service is available at no additional charge; customers pay only for the AWS resources agents consume.

🚀 AWS announced the Agent Toolkit for AWS, a production-ready suite of tools and guidance to help AI coding agents build on AWS with fewer errors, lower token costs, and enterprise-grade security controls. The toolkit includes validated agent skills, a fully managed MCP Server, and easy-to-install agent plugins. At launch AWS is shipping more than 40 skills across infrastructure-as-code, storage, analytics, serverless, containers, and AI, plus three plugin bundles for Core applications, Data Analytics, and Agent development. The Agent Toolkit is available at no additional charge; customers pay only for AWS resources consumed.