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Claude Code Source Leaked via npm Packaging Error: Anthropic confirmed that Claude Code's source code was accidentally leaked through an npm package containing a source map file, exposing nearly 2,000 TypeScript files and over 512,000 lines of code. Users who downloaded the affected version on March 31, 2026 may have received a trojanized HTTP client (compromised software) containing malware.
AI Tool Discovers Zero-Days in Vim and GNU Emacs Within Minutes: Researcher Hung Nguyen used Anthropic's Claude Code to quickly discover zero-day exploits (previously unknown security flaws) in Vim and GNU Emacs that would allow attackers to execute arbitrary code by tricking users into opening malicious files. Claude Code generated proof-of-concept exploits (working examples of attacks) within minutes, demonstrating how AI can accelerate vulnerability discovery.
Critical Python Sandbox Escape in PraisonAI: PraisonAI's `execute_code()` function can be bypassed by creating a custom string subclass with an overridden `startswith()` method, allowing attackers to run arbitrary OS commands on the host system (CVE-2026-34938). This is especially dangerous because many deployments auto-approve code execution, so attackers could trigger it silently through indirect prompt injection (sneaking malicious instructions into the AI's input).
Multiple High-Severity Vulnerabilities in ONNX Format: ONNX (Open Neural Network Exchange, a standard format for sharing machine learning models) versions before 1.21.0 contain several high-severity vulnerabilities including path traversal via symlink (CVE-2026-27489, CVSS 8.7) and improper validation allowing attackers to craft malicious models that overwrite internal object properties (CVE-2026-34445). These flaws allow attackers to read arbitrary files outside intended directories or manipulate model behavior.
PROTheft is a model extraction attack (a method where attackers steal an AI model's functionality by observing its responses to many input queries) that works on real-world vision systems like autonomous vehicles by projecting digital attack samples onto a device's camera. The attack bridges the gap between digital attacks and physical-world scenarios by using a projector to convert digital inputs into physical images, and includes a simulation tool to predict how well attack samples will work when converted from digital to physical to digital formats.