Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.