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CRITICAL DeepSeek AI Agent Weaponised in Proxyjacking Attack on Security Firm // CRITICAL CVE-2026-44827: Hugging Face Diffusers RCE Bypasses Trust Gate // FIRST LOOK Sprocket Launches AI Agent for Hardware and Software Dev // FIRST LOOK OpenAI Astra Model Solves 10 Open Math and CS Problems // FIRST LOOK CrowdStrike Falcon AIDR Adds Coverage for Copilot Studio and Claude Code // FIRST LOOK LLM CLI Tool Adds OpenAI Endpoint Command for Any AI Backend // FIRST LOOK Google Expands Gemini AI Agents Across Chrome Vulnerability Pipeline // CRITICAL Claude Hacked 3 Organizations in Misconfigured AI Security Tests // HIGH OpenAI Rogue Model Compromises Modal and Other Services // FIRST LOOK Microsoft Copilot Super App Merges Chat, Code, and Agents //
ADVERSARIAL-ML

Adversarial-Ml

3 reports
All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Deep Signal Intelligence Report Security Operations
Yellow Teams Bring AI Offense and Defense Into One Security Function

Yellow Teams Bring AI Offense and Defense Into One Security Function

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 5.5 Dark Reading

Yellow teams are an emerging security practice in which engineers build both offensive and defensive AI tools to stress-test AI capabilities and expose vulnerabilities before adversaries do. This dual-role model compresses the feedback loop between red and blue functions, but it also concentrates privileged knowledge of exploitable AI weaknesses in a small group with broad system access. Defenders should assess the insider-risk and knowledge-management implications of consolidating offensive AI tooling within a single team.

LLM Role Confusion Attack Bypasses Safety at 61%

LLM Role Confusion Attack Bypasses Safety at 61%

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Simon Willison

New research from Ye, Cui, and Hadfield-Menell demonstrates that LLMs prioritise the stylistic format of text over its structural role tags, enabling attackers to craft injected content that mimics internal reasoning blocks and bypasses safety guardrails. The study found attack success rates of 61% when injected text stylistically matched model-internal formats, dropping to just 10% after 'destyling'. The authors conclude that without genuine role perception in models, prompt injection defences will remain fundamentally reactive.

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 SecurityWeek

Joey Melo, Principal Security Researcher at CrowdStrike, outlines his methodology for AI red teaming, focusing on manipulating LLM guardrails through jailbreaking and data poisoning without altering underlying source code. His work, rooted in competitive AI hacking challenges, translates classical adversarial thinking into the emerging field of machine learning security. The profile highlights the growing professionalisation of AI red teaming as organisations seek to harden LLM deployments against real-world manipulation attacks.

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