Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

We just published a deep breakdown of the Trivy supply chain attacks yesterday. Twenty-four hours later, we’re writing about the next one. Same threat actor. Different target. Worse implications. This time it’s LiteLLM, the Python library that acts as a universal API gateway for over 100 LLM providers. If you’re building anything with AI agents, MCP servers, or LLM orchestration, there’s a good chance LiteLLM is somewhere in your dependency tree.

We’ve been going back and forth on whether to publish this post. As the maintainers of Kubescape, a fellow CNCF open-source security project, we feel the weight of what happened to Trivy not as distant observers, but as people who see their successes and failures as our own. The Trivy maintainers are our friends. We share the same CNCF community, attend the same KubeCon-s, and fight the same fights (and share the same flights ).

When your SOC alerts on “suspicious AI activity” in a production trading system, your response team faces a question that didn’t exist two years ago: can you explain to regulators exactly which function processed the malicious prompt, which internal tool it called, and how customer data ended up leaving your environment?

It’s 2:47 AM and your SOC dashboard lights up. Six alerts fire across three hours from a single Kubernetes cluster: an outbound HTTP fetch to an unfamiliar domain, a tool invocation inside a customer support agent, an API call to an internal service the agent has never contacted, a service account token read, a file write to a model artifact directory, and an outbound data transfer that looks like normal API usage.

You’re forty-five minutes into a vendor demo for AI workload security. The dashboard looks polished—posture scores, misconfiguration findings, vulnerability counts, all tagged with an “AI workload” label that wasn’t there last quarter. You ask the obvious question: “Show me how this detects a prompt injection attack on our production agent.” Long pause. The SE pulls up a generic process anomaly rule.

You’ve been securing Kubernetes workloads for years. Your CSPM is running, your CNAPP is configured, your team knows how to triage container alerts. Then an AI agent lands in your cluster — maybe from the data science team, maybe from a vendor integration, maybe from a tool you didn’t even know was running. Within a week, it’s making API calls nobody planned, accessing data stores that aren’t in the architecture diagram, and executing code it generated itself.

Your Bedrock agent running on EKS receives a prompt through your RAG pipeline. CloudTrail logs it as a normal bedrock:InvokeModel event—status 200, authorized IAM role, expected endpoint. But inside the container, the agent’s response triggers a tool call that spawns curl to an external IP, exfiltrating the context window. GuardDuty doesn’t flag it because the connection routes through a permitted VPC endpoint. You open your AWS console and see a healthy API call.

You’ve sat through three vendor demos this week. Vendor A showed you an AI-SPM dashboard with a pie chart of misconfigurations. Vendor B showed you a nearly identical dashboard with different branding and a slightly wider set of compliance frameworks. Vendor C showed you posture findings with an “AI workload” tag that wasn’t in their product last quarter.

Your CNAPP flags a misconfigured service account. Your CSPM warns about an overly permissive IAM role. Your container scanner reports vulnerabilities in a model-serving image. But none of these tools can tell you that an AI agent just called an internal admin API it has never touched before — or that a prompt injection caused your LLM to leak customer data through a RAG connector.

Your SOC gets three alerts in quick succession: an unusual outbound connection from a container, a file read on a Kubernetes service account token, and a process spawn that doesn’t match the workload’s baseline. Three different tools, three separate dashboards, three tickets.