Posted on AI Infrastructure Risk AI Security Cloud Security Identity & Governance

Contagious Interview evolves: attackers abuse VS Code Tasks to auto-run malware when a “trusted” workspace is opened

Taiwan’s TWCERT/CC reports a technical evolution in the “Contagious Interview” campaign: instead of relying on victims to manually execute a file, attackers embed a malicious VS Code workspace configuration so code runs automatically when developers open a project folder in Trusted Mode. The technique abuses VS Code’s tasks.json automation (including a run-on-folder-open behavior) and social engineering around Workspace Trust prompts. The activity primarily targets cryptocurrency software engineers and freelancers via recruiting outreach on LinkedIn and gig platforms, then directs them to download test projects from GitHub/GitLab. TWCERT/CC says the resulting payload has been identified as a newer BeaverTail variant (Type 701), with noted functional overlap with OtterCookie (sometimes referred to as “OtterCandy”), and is focused on stealing crypto-related browser extension and wallet data as well as high-value browser-stored secrets. Read more

Posted on AI Security Cloud Security Incidents & Breaches Security Operations

Microsoft’s Storm-0558 postmortem highlights identity signing-key leakage paths and validation gaps that can bridge consumer and enterprise trust domains

Microsoft’s MSRC investigation into Storm-0558 concludes that operational errors likely allowed Microsoft Account (MSA) consumer signing key material to escape a secure token signing environment via a crash-dump/debug workflow, after which the actor (attributed by Microsoft as China-based) likely obtained it by compromising a Microsoft engineer’s corporate account with access to the debugging environment. A separate engineering failure—missing issuer/scope validation when mail systems relied on a common key metadata endpoint—meant a consumer key could be used to forge tokens accepted for enterprise email access. Microsoft says it has since corrected the race condition, improved key-material detection and credential scanning, and updated libraries to automate scope validation. Read more

Posted on AI Infrastructure Risk AI Security Cloud Security Identity & Governance

OWASP formalizes a shared security baseline for GenAI apps with the Top 10 for LLM Applications (now part of the broader GenAI Security Project)

OWASP’s Top 10 for Large Language Model (LLM) Applications has been published as a community security baseline that catalogs common failure modes in GenAI applications—ranging from prompt injection to model theft. OWASP says the effort has expanded beyond a list into the OWASP GenAI Security Project, a broader open initiative covering risks across LLMs, agentic systems, and AI-driven applications, with a large global contributor community and separate project resources and participation tracks. Read more

Posted on AI Security Cloud Security Identity & Governance Security Operations

Google’s SAIF reframed AI security as operational controls, not just model research

Google introduced the Secure AI Framework (SAIF) in June 2023 as a conceptual security framework for AI systems, explicitly mapping AI-specific threats (e.g., model theft, data poisoning, prompt injection, and training-data leakage) to familiar security disciplines such as secure-by-default infrastructure, detection and response, automation, consistent platform controls, continuous testing/feedback loops, and end-to-end risk assessment. While SAIF is not a standard, Google positioned it as a bridge between traditional security programs and emerging AI risks, and tied it to ongoing industry work including NIST’s AI Risk Management Framework and ISO/IEC 42001. Read more

Posted on AI Infrastructure Risk AI Security Cloud Security Identity & Governance

NIST AI RMF: the U.S. government’s voluntary baseline for AI trust, security, and resilience—now expanding to generative AI and critical infrastructure

NIST’s AI Risk Management Framework (AI RMF) established a shared, voluntary vocabulary and process model for managing AI risks across the lifecycle—supporting “trustworthiness” goals such as safety, security, and resilience. Since the AI RMF 1.0 release on Jan. 26, 2023, NIST has expanded implementation support via the AI RMF Playbook and Resource Center, published a Generative AI Profile (NIST-AI-600-1) in July 2024, and, as of Apr. 7, 2026, issued a concept note for a forthcoming profile focused on Trustworthy AI in Critical Infrastructure—signaling growing expectations that AI governance and security controls will be tailored to high-consequence environments. Read more

Posted on AI Security Cloud Security Identity & Governance Incidents & Breaches

CrowdStrike publishes RCA for July 2024 “Channel File 291” Windows sensor outage, reframing update resilience as a board-level risk

CrowdStrike released a root-cause analysis (RCA) and executive summary for the July 19, 2024 “Channel File 291” incident, in which a content configuration update delivered via channel files for its Windows sensor triggered a widespread outage. The company says the specific scenario is now incapable of recurring and outlines mitigations and process improvements intended to enhance resilience. CrowdStrike also reported that by July 29, 2024 at 8:00 p.m. EDT, approximately 99% of Windows sensors were back online, which it compares to a typical ~1% week-over-week variance in sensor connections. Read more

Posted on AI Infrastructure Risk AI Security Cloud Security Identity & Governance

AWS frames “AI sovereignty” as control-and-choice across the AI stack, highlighting Nitro isolation, Bedrock data-use commitments, and sovereign deployment options

In a Security Blog post, AWS outlines how it approaches “AI sovereignty” as an extension of digital sovereignty, centered on data sovereignty (including residency and operator access restrictions) and operational sovereignty (including resilience and independence). AWS positions its sovereignty offering as “control and choice” across the AI stack—deployment location options (including on-premises and isolated deployments), model/service selection, and governance controls. The post highlights AWS Nitro System isolation properties for EC2 instances (including AI accelerator instances), a commitment that Amazon Bedrock customer inputs/outputs are not used to train Amazon Nova or third-party models, and references third-party validation of Nitro’s design by NCC Group. AWS also notes its ISO/IEC 42001 accredited certification coverage for certain AI services and a 2025 surveillance audit with no findings, framing these as assurance mechanisms for customers with sovereignty and compliance requirements. Read more