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A vendor-contributed article by Pentera argues that the hard engineering in AI-driven pentesting lies in the "harness" around the model, not in the model that writes the attacks. It cites the Hugging Face intrusion that OpenAI disclosed in July, which occurred inside an OpenAI evaluation meant to contain the agents, as an example of a har…
Tenable published a vendor blog describing the "harness" behind Tenable Hexa AI, the agentic engine of the Tenable One Exposure Management Platform. The harness is a runtime control layer that treats LLM agents as untrusted insiders and enforces boundaries outside the model. The article covers failures seen in development, the "no standin…
Tenable and OpenAI announced the CyberAgents Exchange AI Inspector (Exchange Inspector), a security review process for submissions to the open-source CyberAgents Exchange registry of AI agents, skills, MCP servers and multi-agent playbooks. The Inspector combines Tenable One AI Exposure scanning, OpenAI frontier models (Daybreak Blue and …
Check Point announced that its AI Agent Security capabilities integrate with Google Cloud's Gemini Enterprise, adding AI-specific risk assessment and inline runtime protection to the platform's centralized agent control plane. The integration targets the risk that an authenticated and authorized agent can still be manipulated into harmful…
Microsoft published a blog post arguing that organizations should treat AI agents as a present insider threat rather than a future risk. Agents with access to critical systems can fail, be manipulated or be compromised, and their behavior can drift mid-run through prompt injection in external documents or untrusted data. Microsoft's answe…
Check Point describes Dangerous Deviation Detection, a detector in Check Point AI Security (now in open beta) that inspects each AI agent tool call before it runs. It flags a call only when the call is dangerous (data leakage, access or privilege escalation, system destruction) and nothing the user or system prompt said warrants it. The a…
F5 Labs argues that AI systems embedded in the operations of the 2026 Winter Games (spectator chatbots, scheduling, translation, fraud detection, operational analytics) create a behavioral attack surface that traditional security controls do not cover. The main risk is manipulated model behavior rather than outages or stolen credentials. …
Infoblox introduces Agentic Denial of Service (ADoS), a class of disruption in which AI agents overwhelm systems with excessive requests, actions or computations, or in which an adversary turns a defender's own automation against it. It is not "DDoS with AI": it denies the business effective use of its AI agents, the infrastructure they d…
A SafeDep researcher describes an experiment that uses Gryph (an audit trail for AI coding agents) and TypeSafe AI's Jev model to separate a developer's normal agent activity from attacks. The article cites threats to agents such as Claude Code and Codex: the Mini Shai-Hulud worm commits Claude Code hooks into repositories, malicious npm …
AI-assisted "vibe coding" is lowering the barrier to app development, but it is also producing apps with common security oversights that app marketplaces do not necessarily flag. The article cites a report that 84% of developers were using or planning to use AI tools, and a Lovable-hosted app in which a researcher found 16 vulnerabilities…
F5 Labs published its 2026 cybersecurity predictions and scored its 2025 forecasts at 6.5/8, by its own assessment. The 2025 review cites AI-powered botnets, attacks on AI model APIs, AI side-channel attacks on cryptography, APT use of hacktivist personas, and expanded Roskomnadzor powers to isolate or reroute Russian internet traffic fro…
