This week in AI and software security:
LiteLLM shipped a backdoored release that was actively exfiltrating secrets.
Axios got hit with supply chain malware through a compromised dependency.
Railway had a CDN caching misconfiguration leaking user data across accounts.
OpenAI Codex was vulnerable to command injection through GitHub branch names.
Mercor, a hiring platform, leaked 1TB of candidate data.
Delve exposed sensitive user information and created live compliance risk.
Six incidents in One week.
Now here is the part nobody is saying out loud:
Three of those six are LLM-layer attacks. Injection. Exfiltration. Manipulation through inputs nobody was inspecting.
And the uncomfortable truth is that most engineering teams shipping AI products today have no runtime layer sitting between their application and their model provider. No request inspection. No response filtering. No policy enforcement. Nothing.
You have a CI/CD pipeline. You've got WAF, and probably a SIEM.
However, you do not have an LLM firewall.
We spent three decades hardening every other layer of the stack. Then the fastest-moving, most manipulable component in the history of software got bolted on at the top, and we called it shipped.
The incidents are not bad luck, but predictable outcome of deploying without defence.
Koreshield is a runtime enforcement proxy that sits between your application and your LLM provider, inspecting every request and response before it completes. One URL change to integrate. Under 50ms latency overhead. Zero-log by default.
The firewall every LLM deployment is missing.
If you are building with LLMs in production, or you know someone who is, this is the conversation that needs to happen now.
Get started with koreshield.com today.
