All news in category “AI and Security Pulse”

🔍 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.

🔒 OpenAI has launched Daybreak, an initiative built on its frontier LLMs and the Codex assistant to help developers embed security throughout the software development lifecycle. Announced on May 12, Daybreak extends the Trusted Access for Cyber (TAC) program and includes GPT‑5.5, TAC-enabled GPT‑5.5, GPT‑5.5‑Cyber and a Codex Security research preview. The initiative supports code scanning, vulnerability triage, automated detection and response while pairing defensive capabilities with verification, proportional safeguards and accountability.

🔐 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.

🔒 CISOs are shifting from a primarily technical control function to strategic business partners as AI reshapes risk, operations, and product delivery. Leaders such as Barry Hensley, Shaun Khalfan, and Jeff Trudeau stress publishing AI security frameworks, embedding security early in development, and aligning controls to business outcomes. They warn of AI-enabled threats — including advanced phishing, voice/video impersonation, and automated vulnerability discovery — and call for continuous controls, stronger identity and data governance, and near-real-time patching. Growing board engagement and changing reporting lines reflect the elevated role of security in enterprise strategy.

🛡️ OpenAI announced Daybreak, a cybersecurity initiative that combines GPT-5.5 family models with Codex Security to identify, test, and propose fixes for vulnerabilities before attackers exploit them. Daybreak builds editable threat models, runs isolated vulnerability tests, and suggests prioritized remediation and patch validation. Access is tightly controlled and available by request, and major vendors are integrating under Trusted Access for Cyber.

🔍 Google Threat Intelligence Group (GTIG) reports a rapid shift from nascent AI-enabled operations to industrial-scale use of generative models by threat actors. Based on Mandiant incident response, Gemini telemetry, and GTIG research, the report documents AI-assisted zero-day exploit development, autonomous malware like PROMPTSPY, and advanced obfuscation techniques. It highlights supply chain targeting of AI environments, anonymized premium LLM access, and specific interest from PRC- and DPRK-linked clusters. The report also outlines mitigations and defensive AI uses.

🔒 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.

🔍 The Google Threat Intelligence Group (GTIG) reported the first confirmed instance of an AI-crafted zero-day exploit observed in the wild. The researchers identified a Python-based exploit that bypasses two-factor authentication in an open-source web administration tool and disclosed the flaw to the vendor to limit mass exploitation. GTIG found artifacts in the code—help text, a hallucinated CVSS score and textbook LLM-style constructs—consistent with large language model generation, and noted broader AI abuse by threat actors including misuse of Gemini and agentic tooling.

🔍 Google’s Threat Intelligence Group (GTIG) reports the first observed case of cybercriminals using AI to discover and weaponize a zero-day, targeting a popular open-source web-based system administration tool to bypass two-factor authentication. GTIG worked with the vendor to close the flaw and disrupt the campaign. Forensic analysis of the Python exploit showed AI-like traits—structured docstrings, Pythonic formatting, and a hallucinated CVSS score. Google noted the attackers did not use Gemini or Anthropic Mythos.

🛡️ Traditional purple teaming is failing because human handoffs and siloed toolchains make detection-to-fix cycles far slower than modern attackers. The author documents a collapse in the vulnerability-to-exploit window—from 56 days in 2024 to roughly 10 hours in early 2026 across CISA KEV, VulnCheck KEV, and ExploitDB—and warns that AI-assisted adversaries can act in seconds. Autonomous purple teaming pairs automated penetration testing, Breach and Attack Simulation, and AI-powered mobilization agents to close the loop at machine speed, converting red findings into blue tests and auto-deploying low-risk fixes while keeping every step auditable.

📄 Schneier surveys simple steganographic tricks—white-on-white text, phonological misspellings, and special fonts—and finds them increasingly ineffective. He notes that even modest 4-billion-parameter models can decode phonologically altered sentences, undermining tokenization-based obfuscation strategies. The post revisits TEMPEST/EmSec concerns, observing that inexpensive software-defined radios and toolkits like GNU Radio have expanded adversary capabilities beyond older Soft Tempest countermeasures. Schneier highlights demos such as Tempest for Eliza and TempestSDR as practical illustrations of ongoing risks.

🔐 The article warns that AI security is repeating the endpoint-era mistake of focusing primarily on posture controls—model cards, SBOMs, guardrails and access policies—while overlooking how systems actually behave. It argues that behavioral detection is essential, monitoring sequences of actions, data access patterns, tool invocations and output drift. The AI surface is expanding rapidly with open-source LLMs, third-party APIs, RAG pipelines and autonomous agents, creating "shadow AI" and dynamic risks. The recommendation is to keep posture as table stakes but prioritize logging, behavioral baselines and SOC integration to turn findings into actionable incidents.

🤖 DXC Technology, Accenture, and other organizations are actively retraining SOC teams to integrate agentic AI by embedding vendor experts and building secure sandboxes. CISOs emphasize top-down leadership, rapid experimentation, and formal learning tracks to shift mindsets and roles. Governance, humans-in-the-loop, and clear escalation and audit paths are required while agents take on L1/L2 tasks.

🔒 Knostic’s internet-wide reconnaissance discovered 1,862 exposed MCP servers, and manual checks of 119 instances showed every sampled server returned internal tool listings without authentication. High-impact flaws like EchoLeak (CVE-2025-32711) and mcp-remote (CVE-2025-6514) illustrate how poisoned documents and command-injection in widely used packages can enable silent data exfiltration or full system compromise. The article prescribes immediate adoption of zero-trust controls: authentication on every interaction, network segmentation, cryptographic signing for tool definitions, continuous integrity monitoring, and human approval for sensitive actions.

🛡️ The operating model under most SOCs—not headcount—is driving persistent alert overload and slow containment times, despite rising security spend and dramatically faster attacker breakout windows. Prophet AI and similar platforms shift routine triage and pivot queries from humans to automation, freeing senior analysts to focus on detection engineering and complex hunts. The author presents a four-question SOC diagnostic, deployment outcomes that returned analyst-years of capacity, funding paths, and vendor-risk checks buyers must evaluate.

🔒 Model Context Protocol (MCP), the emerging plugin layer for agentic AI, has become a significant blind spot for security teams, introducing new shadow-AI risks much like shadow IT. CTEM programs can close this gap by extending scoping, discovery, prioritization, validation and mobilization to cover developer workstations, AI toolchains and MCP server configurations. Practical actions include actively enumerating MCP endpoints, scanning agent configuration and markdown context files for hardcoded API keys, and prioritizing exposures by attacker impact to produce actionable remediation tickets for engineering teams.

🔐 At the World Economic Forum Annual Meeting on Cybersecurity 2026, Fortinet highlighted how AI and deepfakes are reshaping attack surfaces, with identity now a primary vector and attackers operating in structured, continuous campaigns. Discussions stressed that AI accelerates reconnaissance and exploitation while defenders contend with fragmentation, governance gaps, and inconsistent visibility. Fortinet urged platform consolidation, stronger identity and exposure management, and operationalized public-private collaboration to better align detection with response.

🤖 Enterprises migrating between SIEM platforms face repetitive, error-prone rule rewrites because vendors like Splunk, Microsoft Sentinel, IBM QRadar, and Google Chronicle use distinct query languages and data models. Researchers from the National University of Singapore propose ARuleCon, an AI-assisted framework that translates rules while preserving detection intent. In tests on nearly 1,500 conversions it improved accuracy about 10–15% over baseline LLM approaches. Practitioners caution that deterministic engineering, robust validation, and human oversight remain essential to avoid semantic drift and operational risk.

🔎 ICE is developing prototype smart glasses that pair wearable cameras with on-device facial recognition and real-time queries to immigration, criminal, and watchlist databases. Reporting by Ken Klippenstein, linked in Bruce Schneier's post, describes efforts to integrate hardware and software for in-field identification and instant database matches. The program raises immediate concerns about accuracy, bias, data quality, oversight, and civil liberties if deployed without transparent safeguards.

🔒 The Center for AI Standards and Innovation (CAISI), part of the Department of Commerce’s NIST, has secured agreements with Google DeepMind, Microsoft, and xAI to conduct pre-deployment evaluations and targeted research on frontier AI models. These accords expand an existing program that already includes Anthropic and OpenAI and are intended to provide vendors with safety feedback before public release. Microsoft described the partnerships as essential to building trust in advanced systems, while CAISI emphasized continuous evaluation to advance AI security and standards.