Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Campaigns such as Shai-Hulud 2.0 have demonstrated how quickly package malware can propagate through npm and then harvest credentials. Traditional dependency scanning can identify known risks in code once a package is added to the code, but security teams also need a check before installation.

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.

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.

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.

Your first login to a Red Hat OpenShift cluster can feel like sitting in an unfamiliar cockpit: dozens of menus, two perspectives, and dashboards full of metrics you haven’t learned to read yet. The OpenShift Console pulls all of it into one browser-based interface, so you can see what’s running, fix what’s broken, and deploy what’s next without memorizing a single oc flag command.

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.

With PoSA v1.11, we focused on a practical problem: detecting more attack activity does not necessarily help fraud and security teams make better decisions. PoSA already identifies activity across digital impersonation, credential theft, attacker devices and account access. The challenge is making the connections between that activity easier to understand, identifying which users and devices require attention, and making that intelligence available to the systems and teams responsible for responding.