Code Integrity
Local-first scanning, shared rule execution, structural confirmation and graph context across the workspace.
CodeMarine continuously checks the code AI produces, the packages it adds and the actions it takes. Known risks are caught locally on every change. Frontier models investigate the hard cases.
Every AI can write code. CodeMarine secures what lands.One continuous check across agents, editors and CI.
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Codex
CursorCodeMarineOne independent security layer
Secure every changeFind security problems in code as agents work.
Check what entersVerify packages, MCP servers, skills and instructions.
Guard dangerous actionsBlock known destructive commands on supported paths.
Code, dependencies, actions and model investigations share the same policy and evidence.
Local-first scanning, shared rule execution, structural confirmation and graph context across the workspace.
Package identity, provenance, manifests, lockfiles, install behavior and AI tool configuration.
Deterministic action evaluation and provider adapters for defined pre-execution paths.
Governed MCP and API paths with scoped credentials and trusted target context.
Revision-bound proof, action receipts, operational health and explicit coverage gaps.
Focused escalation, model investigation, isolated remediation and deterministic patch validation.
Local checks cover the continuous path. Selected cases move to an approved model.
CodeMarine records the workspace and revision context.
Known policy produces a reproducible result.
Clear findings warn or block. Ambiguous cases can move to an approved model.
A patch must pass the deterministic gate and match the expected revision.
Verified discoveries can become regression fixtures and new controls.
CodeMarine separates local deterministic work from optional managed intelligence and model calls. A team can choose the right privacy and cost boundary for each workspace.
High-frequency code, dependency and policy checks run without sending every change to a model.
Signed updates and service-backed features can extend local checks where configured.
Small local security models can help narrow candidate files. Their output remains probabilistic evidence.
Approved models receive bounded context for novel or high-impact cases.
One policy follows the workspace. Provider integrations add earlier control where the host supports it.
Continuous checks stay close to the workspace. High-confidence catastrophe policy can intervene on supported paths without turning every command into a permission ceremony.
Teams can adopt different frontier tools while code, dependency and action evidence converge on one policy model.
Per-surface status separates capability, setup, health and proof.
Start with continuous deterministic protection. Add provider-specific controls as each surface becomes proof-backed.