Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI Security in DevOps: Best Practices to Follow

Attacking a CI/CD pipeline used to require a specialist who understood Git internals, cloud identity, and the way build runners handle secrets. That is no longer true. The barrier to entry for an advanced attack has dropped to the level of writing prompts in English. Automation itself is not new to DevOps, but AI has raised its ceiling on both sides of the fence.

AI Threats Are Evolving Fast. Your Employees Are Still the Last Line of Defense.

New training on using AI safely and spotting AI-powered threats arrives this Cybersecurity Awareness Month Attackers are moving faster than ever, weaponizing newly discovered vulnerabilities before defenders can patch them and building new types of malware that signature-based defenses can’t catch.

When a Security Guardrail Detects the Attack and Still Can't Stop It

Salt Labs recently showed that a single email could hijack the AI agent platform Manus and reach a victim’s connected accounts. The full technical breakdown, with complete evidence, payloads, and screenshots, is in our research team’s write-up, and Dark Reading first reported the finding in an exclusive. This post is the shorter version: what the finding means and what it tells every organization now deploying AI agents. For the full technical detail, read the Salt Labs research blog.

How We Hijacked an AI Agent With a Single Email

Salt Labs found that the agentic AI platform Manus could be hijacked with a single email. By hiding malicious instructions inside an ordinary message, researchers got Manus to execute malicious code and, from there, reach the email, cloud storage, and code repository accounts a user had connected to it. The full attack required nothing from the victim beyond asking Manus to check their inbox. No stolen password, no clicked link.

Building a bot takes five minutes. What it stands on took eight years. Introducing LimaCharlie Bots.

Co-founder and CCO We shipped bots in the LimaCharlie AI Terminal. You can create one in a few minutes: give it a role, pick a profile if you want one, and open a chat. I said this during our September Build Log demo and I'll repeat it here, because everything else in this post follows from it. The bot is the easy part.

Introducing The GitGuardian Mixin Kit To Extend Docker Sandboxes for Safer AI Coding

Modern enterprise security is increasingly being tasked with keeping agents from affecting critical data and infrastructure. In this video, Dwayne, principal developer at GitGuardian, introduces how GitGuardian AI hooks extend the power of Docker Sandbox isolation. Their micoVM architecture plus the power of ggshield mean anyone can get an agent working in a secure way with very little effort. Chapters.

What's New at GitGuardian: AI Leak Triage & Laptop Secrets Scanning

We found 15x more valid secrets on developer laptops than in code repositories. In this September edition of What's New in GitGuardian, Sr. Product Managers Léna Cuissard and Emmanuelle Franquelin walk through two updates: agents that triage public secret leaks for you, and Developer Endpoint Protection coming together as one package.

AI Security: Are We in the Lull Before the Storm?

AI adoption is accelerating, cyber attacks are increasing and organisations are transforming faster than their security can keep up. In this Razorwire Raw, James Rees looks at why cybersecurity could be in the lull before the storm. AI agents, vibe coding and automation are changing business at extraordinary speed, while attackers are using the same technology to find vulnerabilities and accelerate attacks.

Why AI Coding Agents Keep Writing Broken Access Control

AI coding agents produce authorization logic that compiles, passes review, and enforces the wrong policy. Broken access control ranks first in the OWASP Top 10:2025, where 100% of applications tested showed some form of it, across 1,839,701 recorded occurrences, the highest count of any category on the list. One part of that category is also the part that pattern-based scanning was never built to reach.