This attack passed all checks designed to stop it. Farida Khalaf shares a practical system for catching it the biggest security mistake AI-assisted builders make after deployment.
“The largest supply-chain attacks of 2026 share a dependence on trust. We routinely accept code from Github, especially when it comes from projects we already recognize. Modern software can’t function without it.”
this is absolutely a great way to put it. 2026 has been wild with supply chain attacks. i don’t think i’ve ever seen a time where we get them weekly and sometimes daily!
In enterprise environments, we already have layers like SAST, SCA, artifact repositories, vulnerability scanning, and OWASP practices. The challenge exposed here is the gap between "secure before deployment" and "secure after deployment."
AI introduces another layer to this problem. With AI-assisted coding, software development isn't just for traditional teams anymore.
More employees across organizations can now build and integrate without necessarily having deep security or supply-chain awareness. This makes governance around dependencies, approved repositories, code origins and runtime monitoring even more important.
The focus is now shifting from asking if our code is secure to question whether we know what code is being developed, its dependencies, and whether we can trust them, throughout its entire lifecycle.
“The largest supply-chain attacks of 2026 share a dependence on trust. We routinely accept code from Github, especially when it comes from projects we already recognize. Modern software can’t function without it.”
this is absolutely a great way to put it. 2026 has been wild with supply chain attacks. i don’t think i’ve ever seen a time where we get them weekly and sometimes daily!
great article :)
Like Fafi said, it means a lot coming from you Chris! Thank you for reading!
thanks to the both of you! 🔥💚
Coming from you Chris means a lot.
Thank you and mostly Karo for giving me the opportunity to share it with her audience
My pleasure!!!
Passing every provenance check and still being malicious is the scary part of this whole story.
In enterprise environments, we already have layers like SAST, SCA, artifact repositories, vulnerability scanning, and OWASP practices. The challenge exposed here is the gap between "secure before deployment" and "secure after deployment."
AI introduces another layer to this problem. With AI-assisted coding, software development isn't just for traditional teams anymore.
More employees across organizations can now build and integrate without necessarily having deep security or supply-chain awareness. This makes governance around dependencies, approved repositories, code origins and runtime monitoring even more important.
The focus is now shifting from asking if our code is secure to question whether we know what code is being developed, its dependencies, and whether we can trust them, throughout its entire lifecycle.