Frequently asked questions

AppSec for the AI era. We stop bad code before it reaches production. We sense what happens in the developer workspace and at build time, follow every artifact into production, and give security teams the facts to prevent, respond and prove.

Yes. We're reinventing AppSec for the AI era. We stop bad code before it reaches production, and every decision rests on independent, deterministic, provable facts about what was built and what runs.

Answer here.No. We don't use AI to secure AI. Policies your team writes and approves are enforced against what was actually built and what actually runs. No model makes the decision.

Most ASPM tools build posture from scanner findings. We build it from facts about what was built and what runs, and we prevent bad code from shipping rather than just prioritizing the backlog.

Crash Override supports Python, JavaScript, TypeScript, Go, Rust, Java, Ruby, PHP, C, C++, C#, Kotlin, Swift, and Scala.

Book a demo and we'll have you up and running straight after. Adding the build sensor to a pipeline is a GitHub Action or a GitLab component, so each change is small.

Stitching data across silos with an LLM is nondeterministic and brittle. We're independent, deterministic, structured data from inside your build and production. Connect us over MCP and Claude gets facts instead of guesses.

Crash Override works with GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and ArgoCD. Each integration is a single YAML step.

No. In the developer workspace, the sensor recommends; it never blocks. Enforcement happens at the build and the deploy, under the policy your security team writes and approves.

We operate a responsible disclosure programme. Report it to [email protected].

Two. Chalk, the open-source project our build sensor is built on, marks binaries and tracks their provenance from the build into production. Ocular orchestrates security scanning on Kubernetes. Both are free.

Chalk and Ocular are GPL licensed.