All news with #ai red teaming tag

🔎 XBOW received early access to Anthos Mythos Preview and ran a structured evaluation across benchmarks, interactive workflows, and live-site integrations. The model excels at reading source code, finding vulnerability candidates, and aiding native-code analysis and reverse engineering. While powerful for generating leads and precise technical analysis, Mythos Preview is less effective at exploit validation and exhibits mixed judgment that benefits from human orchestration.

🛡️ depthfirst's autonomous agent discovered 21 previously unknown zero-day vulnerabilities in FFmpeg, producing reproducible PoC inputs for each at a reported cost of about $1,000 for the run. In the same week, Google released Chrome 149 with fixes for a record 429 security bugs, over 100 of which are critical or high severity, following an overhaul of its bounty program to cope with a surge of AI-generated reports. The findings illustrate how AI is accelerating vulnerability discovery and increasing pressure on triage and patching processes across widely used software.

🛡️ Lloyds Banking Group treats agentic AI as an engineering problem to be designed, constrained and tested at scale. At OWASP’s GenAI Security Summit, Lloyds’ security leads explained an “AI safe adoption” strategy spanning lifecycle governance, an internal agent marketplace, and multidisciplinary feature teams. Key challenges include agent identity, runtime observability and automated red‑teaming, while prioritizing low‑risk, high‑value use cases for customers.

🔎 The Microsoft AI Red Team released a v2.0 update to the Taxonomy of Failure Modes in Agentic AI Systems, grounded in twelve months of red team engagements and operational data. The revision adds seven new failure mode categories—such as agentic supply chain compromise, goal hijacking, and visual attacks against computer-use agents—expands mitigations, and emphasizes supply chain, zero‑trust, and session hardening.

🔍 At Infosecurity Europe 2026, Bugcrowd unveiled ExploitBench, a graded benchmark assessing AI models' ability to chain vulnerability discovery into staged exploits against a vulnerable V8 build. Anthropic’s Claude Mythos outperformed OpenAI’s GPT‑5.5 in head‑to‑head runs, achieving higher average scores and more top‑tier exploits, often with occasional human nudges. The report highlights rising offensive potential of frontier LLMs and urges defenders to adopt automated remediation and prioritization.

🛡️ Anthropic has broadened Project Glasswing, giving 150 additional organizations access to its most capable model, Claude Mythos Preview, to help find vulnerabilities in critical software. The program, first opened to roughly 50 partners in April, claims more than 10,000 high- or critical-severity flaws discovered to date. New participants span 15+ countries and underrepresented sectors like power, water, healthcare and hardware, chosen for the potential catastrophic impact of breaches. Anthropic warned that while discovery is accelerating, safe public release of Mythos-class models remains restricted due to incomplete safeguards.

🔍 Halcyon research reveals a dramatic rise in AI-powered cybercrime tooling across underground markets, jumping from 38 mentions in December to 1,486 in February. Cynthia Kaiser, SVP of Halcyon’s Ransomware Research Center, detailed four product categories: weaponized LLMs, AI-enabled identity fraud, AI-augmented malware/infrastructure, and jailbroken or stolen AI services. She warned that automated distribution, freemium models and redundant channels have lowered the financial barrier to entry and increased resilience against takedown efforts.

🔍 Cisco researchers found that popular frontier LLMs from OpenAI, Anthropic, Google, xAI, and Amazon exhibit substantially higher risk when subjected to multi-turn adversarial attacks than when assessed with single-prompt safety benchmarks. The team ran tens of thousands of single-turn and multi-turn attacks across 15 models and multiple configurations, revealing wide gaps in attack success rates (ASRs) and configuration-dependent safety behavior. They urge improved benchmarks, transparency on configuration impacts, and publication of paired single- and multi-turn ASRs to better inform procurement and governance decisions.

🔒 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.

🔒 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.

📈 Digital.ai's 2026 Application Security Threat Report found that 87% of monitored customer-facing apps were attacked in 2026, up sharply from 55% in 2022. The firm says agentic AI has lowered the skill and time required for threat actors to inspect code, generate exploits and adapt malware. Financial services, automotive and medical device apps were most targeted, and iOS attacks have nearly closed the gap with Android.

🔒 Anthropic's Mythos Preview, shared last month with a limited set of security partners, has demonstrated the ability to autonomously find zero-day vulnerabilities across major operating systems and browsers. Anthropic paired the release with Project Glasswing and $100 million in usage credits to help defenders, but reports of unauthorized access and denied requests from Chinese entities have already emerged. The development challenges the assumption of a durable US lead and has injected cybersecurity into high-level US–China summit talks, prompting urgent questions about access, regulation, and international cooperation.

🔒 Penetration testing shows AI and LLM deployments contain a disproportionate share of severe vulnerabilities. Cobalt’s State of Pentesting Report finds 32% of LLM findings rated high risk versus 13% for legacy enterprise tests, and only 38% of those high-risk LLM issues are remediated. Experts point to emerging attack surfaces — notably prompt injection, now OWASP’s top LLM risk — broader blast radii from model integrations, and fragmented ownership for fixes. Recommended countermeasures include threat modeling, red teaming, least-privilege access, strict output validation, and human approval gates for high-consequence actions.

🔒 Nutanix and Palo Alto Networks have integrated Prisma AIRS into the Nutanix Enterprise AI platform to embed automated AI model scanning and continuous red teaming directly into the MLOps pipeline. The integrated solution scans models at check-in, analyzes dependencies for known vulnerabilities and license issues, and validates provenance and file formats to block backdoors or unsafe execution paths before deployment. It also provides API-driven red teaming with a context-aware agent and a large, continuously updated attack library so teams can test resilience and prioritize business-relevant risks without complex setup.

🔒 A new generation of frontier AI models is changing how cyberattacks are developed, enabling speed, scale, and accessibility previously unseen. Early testing of advanced models, including Claude’s Mythos, shows they can identify code vulnerabilities, map attack paths, and generate working exploits with minimal effort. Organizations must treat these as fully AI-powered attacks and prioritize proactive readiness, detection, and mitigation strategies.

🤖 In the latest Adversary Universe podcast, CrowdStrike leaders discuss how AI is accelerating vulnerability discovery and could produce a rapid surge of new flaws — a potential 'vuln-pocalypse'. They urge prioritizing remediation based on active exploitation and prevalence in environments. CrowdStrike recommends leveraging AI for agentic red teaming, vulnerability scanning, and crowdsourced telemetry to detect post-exploitation behaviors. They point to Project Glasswing and OpenAI's Trusted Access for Cyber as examples of defense-focused collaboration.

🔒 Palo Alto Networks' Unit 42 is expanding its Frontier AI Defense service through a new partnership with Armadin, the offensive security firm founded by Kevin Mandia. The collaboration introduces an autonomous External AI Hyperattack Assessment that passively discovers internet-facing assets, then deploys a coordinated swarm of AI attack agents to validate exposures and exploit vulnerabilities in parallel. Unit 42 says this pressure-tested, decision-grade evidence accelerates remediation and helps organizations reduce AI-enabled external attack risk across cloud and perimeter environments.

🔍 The Firefox team used frontier AI models in partnership with Anthropic to scan the browser and fix latent security flaws. After earlier work with Opus 4.6 that produced 22 fixes for Firefox 148, an early evaluation of Claude Mythos Preview uncovered 271 vulnerabilities now addressed in Firefox 150. The team worked around the clock to triage and remediate the findings, and observers note this technology favors defenders—provided patches reach users quickly.

🔍 Anthropic warns that its AI vulnerability-discovery system Mythos will sharply increase the pace and volume of software flaws, forcing defenders to prioritize what to fix. The company recommended using the probabilistic EPSS model (developed by Empirical Security and published through FIRST) to triage vulnerabilities—patching CISA’s KEV list first, then addressing CVEs above a chosen EPSS threshold. Empirical Security leaders emphasize that EPSS is machine-driven and already integrated across many vendor products.