All news with #ai security tag

🔍 Microsoft disclosed MDASH, an AI-driven vulnerability discovery system that found 16 previously unknown Windows flaws, including four critical remote code execution bugs that were patched as part of the May 12 Patch Tuesday release. Built by the Autonomous Code Security and Windows Attack Research teams, the platform orchestrates more than 100 specialized AI agents across multiple models to scan, validate and construct triggering inputs before human review. Microsoft said MDASH is intentionally model-agnostic and will enter private enterprise preview next month.

📈The UK cybersecurity sector generated £14.7bn in revenue last year and contributed £9.1bn in gross value added, the government reported on 13 May. Employment rose to nearly 70,000 and the number of firms climbed to 2,603, with AI-focused cybersecurity vendors growing sharply. The government unveiled the Cyber Resilience Pledge and plans legislation via the Cyber Security and Resilience Bill to tighten standards. Experts warn that advances in AI increase risks and call for stronger, harmonized incident reporting and defences.

🔒 The 2026 CSO Awards recognize 64 security organizations whose projects deliver measurable business value and stronger enterprise resilience. CSO profiles six standout initiatives that illustrate trends such as zero trust, AI-driven automation, gamified awareness, and shift-left cloud security. Examples include Copart’s adaptive phishing and gamification that lifted reporting rates from ~20% to over 55%, HMSA’s Zero Trust Data Governance that removed confidential member information from nonproduction environments, and Hensel Phelps’ automation program saving more than 1,250 work hours annually.

🔒 Google describes five coordinated approaches to reduce scams and fraud, presented at the EMEA Anti-Scams and Fraud Summit hosted by the Google Safety Engineering Center in Zurich. The company highlights AI-powered defenses that block spam, malware and policy-violating ads, plus on-device scam detection in Phone by Google. It also emphasizes user tools, education through Be Scam Ready, cross-platform threat-data sharing via the Global Signal Exchange, and partnerships with law enforcement to disrupt criminal networks.

🔒 Microsoft announced MDASH, a multi-model agentic scanning harness that orchestrates over 100 specialized AI agents to discover, validate, and prove exploitable bugs in Windows. In internal tests it found 21 of 21 seeded driver vulnerabilities with zero false positives and achieved an industry-leading 88.45% score on the CyberGym benchmark. The harness produced 16 CVEs in today’s Patch Tuesday across networking and authentication stacks, including four Critical remote code execution flaws, and is in limited private preview with select customers.

🔒 Microsoft’s May Patch Tuesday updates address at least 118 security flaws across Windows and other products, including 16 rated critical. This release is notable as the first Patch Tuesday in nearly two years without fixes for known exploited zero-days or previously disclosed vulnerabilities. Other major vendors — Apple, Google, Mozilla and Oracle — have accelerated patch cadences after collaborative AI evaluations. Administrators are advised to apply updates promptly and back up data before upgrading.

🔍 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.

🔒 OpenAI has unveiled Daybreak, an enterprise-focused cyber-defense platform that combines its large language models with Codex-style agent capabilities and broad integrations across the security ecosystem. The initiative aims to accelerate vulnerability discovery, generate and test fixes within repositories, and deliver audit-ready evidence back into enterprise workflows. Daybreak will be offered in tiers including GPT-5.5, Trusted Access, and GPT-5.5-Cyber, and is being developed with major vendors and government partners.

🔒 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.

📊 Database Center now offers an AI-native manageability interface powered by Gemini, delivering fleet-wide visibility across Google Cloud managed databases. It introduces Gemini-backed recommendation validation (coming soon) to simulate performance impacts before applying changes like new indexes or machine upgrades. Additional enhancements include inventory, end-to-end lineage, and automated health checks to reduce MTTR and operational overhead.

📌 At Cosmos Conf 2026 Microsoft outlined how AI is transforming application and database design, arguing data platforms must become systems of reasoning that handle prompts, memory, and evolving context. Leaders from OpenAI, Vercel, and Walmart stressed the need for serverless instant scalability, integrated caching, low-latency global distribution, and developer cost visibility. Demos and customer stories highlighted patterns like vector search, change feed, and role-based governance to deliver real-world, low-latency AI experiences.

🛡️ Google disclosed detection of an unknown threat actor using a zero-day exploit likely developed with an AI model, marking the first observed malicious application of AI for vulnerability discovery and exploit generation. GTIG said the exploit was a Python script implementing a 2FA bypass in a widely used open-source web administration tool and contained hallmarks of LLM-generated code. Google worked with the vendor to patch the flaw, disabled malicious assets, and linked the activity to a broader set of AI-enabled abuse campaigns including the Android backdoor PromptSpy.

🔍 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.

🔍 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.

🔍 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.

📄 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.

👓 Smart glasses are returning with advanced sensors and AI, creating new privacy and security challenges for users and bystanders. They can record or livestream covertly and feed footage to AI systems for face recognition and data retrieval, enabling stalking, fraud, and surveillance. Platform policies and outsourced review raise additional exposure. Mitigations include updates, permissions control, MFA, and disabling AI training where possible.