LIVE FEED
FIRST LOOK NVIDIA Launches OSAA and SAFE Open AI Security Framework // HIGH UK AI Security Institute Reports Security Incident INC-2026-07-28 // FIRST LOOK Microsoft Launches Zero Trust for AI Agent Security Tools // FIRST LOOK Varonis Launches Agent IBAC to Constrain AI Agent Actions at Runtime // 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 //
ANTI-ANALYSIS

Anti-Analysis

2 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 Governance AI Security Tools Deep Signal Intelligence Report Security Operations
Prompt Injection Malware Evades LLM Security Scanners

Prompt Injection Malware Evades LLM Security Scanners

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Schneier on Security

A malware developer has embedded nuclear and biological weapons-related text inside JavaScript comment blocks within spyware payloads, specifically to trigger refusal behaviour or context confusion in LLM-powered security analysis pipelines. The technique exploits the architectural gap between how interpreters (which skip comments) and language models (which ingest the full file as input) process the same file. While ineffective against traditional static analysis tooling, the tactic represents a practical adversarial countermeasure targeting AI-first triage workflows and analyst copilots.

Malware Uses Prompt Injection in JavaScript to Evade LLM Tools

Malware Uses Prompt Injection in JavaScript to Evade LLM Tools

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Schneier on Security

A malware developer has been observed embedding fake system instructions and policy-triggering content — including references to nuclear and biological weapons — inside JavaScript comment blocks to confuse or trigger refusal behaviour in LLM-powered security analysis pipelines. The technique does not affect code execution but is specifically designed to disrupt naive AI-first triage tools that feed raw file content to language models without isolating it as untrusted data. Traditional static analysis methods remain unaffected, but the approach signals an emerging class of anti-AI-analysis evasion techniques.

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