A newly disclosed GitHub Copilot CLI vulnerability abuses encrypted prompt injection to exfiltrate developer secrets and local files, while a ransomware affiliate separately weaponizes AI coding assistants and MCP servers as command-and-control channels in live enterprise intrusions.
A technical breakdown of the Meta Muse zero-day that let attackers hijack the agent's tool calls and inject malware, and what the incident reveals about prompt injection as a real attack surface in production AI agents.
A practical audit guide to GitSpawn-style prompt-to-commit attacks, covering how malicious repositories and prompts steer AI coding agents into unintended git operations, which of the seven affected agents remain unpatched, and the guardrails—sandboxing, repo allowlists, disabled…
A practical security analysis of how AI agent memory stores can be poisoned with persistent prompt injection, and what developers can do to detect, contain, and prevent it.
A technical analysis of how a reported Claude jailbreak enabled alignment bypass for AI-assisted penetration testing and what developers can do to harden systems against prompt injection.
A practical breakdown of how the Claude Desktop double-agent attack can trick AI assistants into trusting untrusted messages and ultimately execute remote code on a target machine.
An analysis of the GitLost prompt injection vulnerability in GitHub’s Copilot Agent, showing how attackers could coerce the AI into exposing private repository data and how developers can defend against similar agentic security flaws.
This post examines how prompt injection can be chained into malicious payloads inside LLM API integrations, and how to audit agentic workflows for ransomware risk before attackers exploit them.
Mozilla’s 0din research shows how AI coding agents like Claude Code can be manipulated through context injection in seemingly clean GitHub repositories to install malware, exposing a new class of developer workflow risk.
A deep technical walkthrough of how AI model unavailability can be exploited in fallback scenarios, and how to harden applications against outages like Anthropic's Fable 5 access disruption.
This deep technical walkthrough explains how to harden an AI agent against prompt injection, tool abuse, and data exfiltration after the Mythos and Fable breach by layering input filtering, least-privilege tool access, sandboxing, and runtime monitoring.
A deep technical walkthrough of how the Claude Fable incident underscores practical prompt injection defenses, from red-teaming patterns and trust boundaries to logging, policy hardening, and safer agent design.
A deep technical audit of GitHub Copilot’s Claude Fable 5 output shows how prompt injection, secret leakage, and unsafe code patterns can emerge in real workflows and how to detect them.
A deep technical walkthrough of how prompt injection can compromise Claude Code’s AI-suggested commands, and how to audit developer workflows for hidden execution risks.
A technical walkthrough of how an AI agent was phished and how to harden agentic workflows with stronger boundaries, privilege controls, verification, and monitoring.
A deep technical walkthrough of how the OpenClaw phishing simulation exposed sensitive credential leakage in an AI agent workflow, and how developers can test and harden tool authorization boundaries against similar attacks.
A deep technical walkthrough for using JavaScript to test AI-powered web apps for prompt injection vectors, sensitive data leakage, and other model-exposure risks.
A deep technical walkthrough of Cisco’s blueprint for defending LLM agents against prompt injection, tool abuse, and data exfiltration in real-world AI deployments.
A deep technical walkthrough for hardening Node.js AI agents against prompt injection, covering threat modeling, input isolation, tool gating, output validation, and layered defenses that reduce real-world abuse risk.
A technical walkthrough of how red teaming an LLM differs from a traditional web app pentest, focusing on model-specific attack surfaces like prompt injection, jailbreaks, and AI security evaluation workflows.
This post explores how malicious prompt injection can be executed via pull request comments in AI-powered coding agents, highlighting security risks and mitigation strategies.
AI coding agents can turn PR descriptions, comments, and other repository text into prompt-injection vectors unless workflows strictly limit permissions, secrets, and tool access.
A practical guide to treating agent sandboxes as application runtimes, with clear boundaries for files, commands, network access, credentials, and approval gates.
An overview of how to design trust boundaries and defensive controls to reduce prompt injection risk in LLM-powered systems.