Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Today, Cyberhaven is joining the Open Secure AI Alliance to help advance a future in which enterprises can adopt open-source AI without compromising security, compliance, or control over their data. Cyberhaven is betting on a world where companies are free to choose from many models, agent frameworks, and open harnesses, as well as run them on infrastructure they control. That choice matters. Enterprises will not standardize on a single model or AI platform.

Researchers at Lexfo are tracking three sophisticated phishing kits that were built using open-source components, primarily based on the publicly available adversary-in-the-middle (AiTM) attack framework “Evilginx.” The phishing kits are designed to proxy “live Microsoft 365 authentication sessions to capture session cookies and OAuth tokens in real time, bypassing MFA entirely.” The kits also use AI to generate personalized phishing lures with a variety of different delivery metho

The open-source trust model is broken. Not strained, not under review—broken. For years, your team has pulled third-party code into your repositories on the reasonable assumption that a widely used dependency is safe. Popularity looked like a proof. Millions of downloads looked like a security review. TeamPCP has proven otherwise.

Debugging privacy-preserving protocols is hard. Oblivious HTTP has several different steps across four different parties, not to mention binary HTTP encoding and details spread across many draft RFCs. We've taken what we've learned operating protocols like Oblivious HTTP at the scale of millions of requests per second, and wrapped it up in a nice, clean CLI tool — that we are open-sourcing today. We call it our privacy-client, or pvcli.

AI is changing the speed and scale of cyber defense, and the speed and scale of the adversary. As AI becomes embedded across government, critical infrastructure, and enterprise environments, defenders need the ability to inspect, test, adapt, and secure the systems they depend on.

The recent developments surrounding vulnerabilities in major AI repositories like Hugging Face serve as a critical wake-up call for the cybersecurity community. As we accelerate toward an agentic future, the platforms we rely on for innovation are increasingly becoming the primary vectors for systemic risk.

At 2 a.m., an alert queue full of raw Windows events, firewall logs, and duplicate detections stops being a tooling problem and becomes an operations problem. The team does not need another dashboard. It needs a SIEM that can ingest the right data, normalize it, correlate it well enough to surface real incidents, and stay maintainable after the initial rollout.

The pace is not slowing down. Between May 18 and June 1, 2026, four distinct supply chain campaigns swept through npm, PyPI, Crates.io, GitHub Actions, and Composer.

On May 25, 2026, the maintainer of jqwik, a Java property-based testing library, released version 1.10.0 to Maven Central with a hidden instruction intended for AI coding agents. The payload told agents to disregard previous instructions and delete all jqwik tests and code. It was hidden from humans with ANSI terminal codes but left fully readable to any tool that captures raw output.