All news in category “AI and Security Pulse”

🛡️ Generative AI enables defenders to deploy large numbers of convincing, adaptive honeypots — from Linux shells to IoT devices — using simple text prompts. These AI-driven decoys are particularly effective against automated attackers that favor speed over stealth, allowing analysts to observe tactics and tooling in real time within a controlled environment. By exploiting the lack of awareness in AI agents, organizations can shift from passive detection to active manipulation, turning attacker automation into a defensive liability. Prototype implementations show how a listener, simulated vulnerability, and an AI responder combine to emulate targeted systems at scale.

🔒 AI-assisted "vibe" coding makes building apps fast but frequently yields insecure or nonfunctional code that can expose sensitive data. Non-technical creators should treat AI output as a draft: verify and test code, protect secrets by using environment variables, prefer reputable libraries, and enforce secure defaults. Regular backups, sandbox testing, dependency updates, and secret scanning help reduce exposure.

🔐 Anthropic announced Claude Mythos Preview can autonomously discover and weaponize software vulnerabilities, prompting the company to restrict access to a small set of partners. The claim unsettled security researchers and analysts, in part because details remain sparse and speculation ranges from capacity limits to safety-driven restraint. The authors view Mythos as a real but incremental advancement that highlights the need to separate patchable from unpatchable systems and the verifiable from the hard-to-verify. They recommend tighter isolation, least-privilege design, continuous testing, and the use of defensive AI agents to reduce risk.

🔒 As AI tools such as Claude Mythos and Project Glasswing compress vulnerability discovery from weeks to minutes, the traditional patch window is effectively gone. The piece urges organizations to adopt an assume-breach posture that prioritizes rapid detection, automated attack reconstruction, and immediate containment. Network Detection and Response (NDR) platforms — highlighted via Corelight — are presented as practical instruments to visualize, measure, and reduce mean-time-to-contain.

🔒 Enterprise SaaS products increasingly adopt Retrieval-Augmented Generation (RAG) to give AI agents access to customer-specific knowledge, but that bridge also creates severe security liabilities. The article reviews recent high-profile failures — from the EchoLeak zero-click exfiltration to vector database reconstructions, indirect prompt injections in IDEs and large-scale knowledge-base poisoning — and breaks down the typical three-phase RAG architecture: ingestion & embedding, vector storage & retrieval, and LLM generation. It advocates a defense-in-depth posture combining pre-ingest DLP, retrieval-time RBAC/ABAC, prompt isolation and output filtering, and highlights Google Cloud services like Cloud DLP, Vertex AI vector search, Vertex AI model armor and Security Command Center to operationalize those controls.

🛡️ Identity management is changing as AI agents introduce a new class of non‑human identities that can act, decide, and access resources at machine speed. Experts including Dustin Wilcox and Michael Adams recommend an identity-first security posture built on clean directories, enforced least privilege, and clear offboarding. They warn that legacy models and inventory processes won’t track proliferating tokens and agents, so organizations should catalog non‑human identities, assign ownership, and treat MFA as a baseline while moving toward phishing‑resistant methods and continuous verification.

🔊 Deepfake voice cloning now needs as little as three seconds of audio and freely available tools to produce convincing, real-time impersonations. Incidents spiked in 2025, with attackers focusing on finance, HR and IT teams to trigger fraudulent transfers or credential changes. The author warns that traditional security stacks rarely detect these social-engineering attacks and urges organizations to build human verification habits—verbal passcodes, callbacks and continuous simulation-led training from vendors like Adaptive Security.

🔎 Anthropic's Claude Mythos Preview has reignited debate about AI-enabled vulnerability discovery and the operational strain that follows. Rapid detection is valuable, but finding issues and verifying fixes are distinct workflows, and many organizations lack the tooling to close that loop. Without centralized tracking, prioritized context, and verified remediation, faster discovery can simply produce a larger backlog of unresolved critical issues. Platforms like PlexTrac are presented as the operational layer needed to normalize findings, assign ownership, and enforce continuous re-testing.

🔒 AI is transforming DevSecOps by moving security earlier into the development lifecycle and shifting teams from reactive validation to continuous, intelligent enforcement. Organizations are embedding security controls into AI coding assistants, using LLMs for contextual vulnerability scanning, and surfacing automated remediation directly in IDEs and pull requests. Experts caution this brings new risks—model access, prompt injection, data leakage and provenance—that demand enterprise governance, cross-functional alignment, and updated skill sets.

🤖 AI agents are shifting the Tier 1 SOC analyst role from manual triage to oversight and decision-making. Instead of spending hours pivoting across logs and telemetry, analysts can delegate evidence collection to agentic AI that queries systems, correlates signals and builds evidence chains in real time. The human role becomes orchestration—reviewing outcomes, validating uncertainty and aligning actions with business risk. Trust is earned via transparency, staged deployments and practitioner-led adoption.

🔒Attackers are increasingly hijacking legitimate AI agents and compromised credentials to extract sensitive information, turning in-house assistants into active threats. These agents become 'agentic endpoints'—autonomous identities with broad privileges that often evade traditional controls by using plugins, extensions, and stolen API tokens. Organizations need a consolidated security platform, continuous verification through PAM and Zero Trust, and board-level governance to manage this accelerated, AI-driven risk.

🔒 The US Cybersecurity and Infrastructure Security Agency (CISA) does not yet have access to Anthropic’s bug-hunting AI model, Claude Mythos, while other government bodies do. Anthropic has restricted preview access through Project Glasswing to a select set of agencies, industry groups, and software providers over concerns the model could be misused to find and exploit vulnerabilities. Bloomberg reports members of a private Discord channel obtained unauthorized access and have been using Mythos for non-cybersecurity purposes, supplying screenshots to support their claim.

🔒 Mandiant VP Jurgen Kutscher warns the rush to deploy AI in enterprises is reviving old cybersecurity failures as organizations neglect basic controls. During red-team engagements, Mandiant uncovered unencrypted streams, misclassified data and AI-enabled policy changes that allowed exfiltration. He urges firms to implement AI governance, revisit secure architectures and run red-team validation before uncontrolled adoption.

🔒 The contributor reframes AI agents as delegated identities rather than independent actors, arguing enterprises cannot safely govern agents without first governing the identities that delegate authority to them. It calls out pervasive "identity dark matter"—unmanaged human and machine credentials that create hidden permissions and execution paths which agents can amplify. The piece recommends sequencing remediation: first illuminate and reduce identity dark matter across humans, bots, and service accounts, then feed continuous telemetry into a real‑time delegation authority engine. Orchid's continuous observability model is presented as that live feed, enabling dynamic decisions to allow, recommend, constrain, or block agent actions based on delegator posture, intent, application context, and scope.

🔎 Google Threat Intelligence scanned a large Common Crawl corpus to detect indirect prompt injection (IPI) patterns embedded in public web pages. The team combined signature-based pattern matching, Gemini-assisted classification, and manual review to reduce false positives and contextualize findings. Most observed injections were low-sophistication—pranks, benign guidance, or SEO-driven prompts—but a smaller and rising set attempted data exfiltration or destructive actions. The study excludes social media and login-protected content and reports a 32% increase in malicious samples between Nov 2025 and Feb 2026.

🔒 Palo Alto Networks' Unit 42 summarizes the ten most frequent CISO questions about frontier AI, outlining operational risks, strategic impacts, and prioritized mitigation steps. The piece characterizes frontier models (for example, Anthropic Mythos) as advanced foundational systems that can autonomously find vulnerabilities, chain exploits, and scale reconnaissance and social engineering at machine speed. Unit 42 urges organizations to prioritize findings by attacker reachability and AI exploitability, adopt machine-speed defenses, integrate frontier models into the SDLC, and consider the Unit 42 Frontier AI Defense service and a CISO checklist for immediate and long-term hardening.

🔍 AI-driven detection reduces alert noise and accelerates incident identification by building behavioral baselines across users, endpoints, identities, and cloud workloads. Platforms that combine behavioral models, cross-telemetry correlation, and automated triage suppress low-value alerts, enrich context, and prioritize what matters for lean security teams. Paired with managed detection and response, integrated automation shortens dwell time, limits lateral movement, and reduces operational impact when prevention fails.

🔒 At Google Cloud Next 26, COO Francis DeSouza said Google will not release a separate cyber‑focused frontier model and instead relies on the generalist Gemini3.1 Pro for security use cases. He advised pairing a strong general model with the right tooling, governance and access controls and training it on organisation‑specific context. Google plans to combine Gemini with agent and platform capabilities to support automated detection, triage and response. Competitors such as Anthropic and OpenAI are pursuing specialised variants like Claude Mythos and GPT‑5.4‑Cyber.

⚠️ Anthropic’s Project Glasswing, powered by the Mythos preview model, discovered pervasive, long-lived vulnerabilities across major operating systems and browsers — including chained exploit sequences, race-condition privilege escalations, and distributed ROP chains — and Anthropic paused a public release to give major vendors time to patch. Despite that cooperation, fewer than 1% of findings were patched, exposing a systemic remediation bottleneck. The author argues defenders must shift from scheduled, CVSS-driven processes to signal-driven validation, environment-specific context, and closed-loop remediation to act at machine speed against autonomous, AI-enabled attackers.

🔒 Unit 42 demonstrates Zealot, a multi-agent LLM proof of concept that autonomously chained well-known cloud exploits in an isolated GCP sandbox. The system coordinated specialist agents to perform reconnaissance, exploit an SSRF vulnerability, steal metadata service credentials, impersonate service accounts and exfiltrate BigQuery data without step-by-step human prompts. The report emphasizes that AI acts as a force multiplier—accelerating exploitation of misconfigurations rather than inventing novel techniques—and urges defenders to harden metadata access, enforce least privilege and adopt machine-speed detection and response.