Lock it down
Ship AI-assisted code without shipping your secrets
One senior engineer, backed by a fleet of AI agents, audits, hardens and monitors how your team uses Claude Code, so the speed you gained never becomes your biggest liability. We run this stack all day: we know exactly where it bites.
Free 30-min call · You leave with a written audit report and a hardened configuration
Teams we ship for
logos à fournir : clients citables
The new AI software engineering
Coding changed. Engineers stopped being the bottleneck.
The bottleneck moved to orchestration. Claude Code is the brain, MCP plugs into your stack, and one senior engineer reviews every line the agents ship. See how we build, automate and ship on every engagement.
- 01Claude Codethe brain · subagents · MCP
- 02Anthropicmanaged agents · runtime
- 03CursorIDE pair-programming
- 04n8ncron · webhooks · glue
- 05Supabasedata · memory · retrieval
AI coding tools ship with defaults built for one developer, not a team.
Permissive defaults are fine on one laptop. Rolled out across a team with production access, they turn into risk classes most engineering leaders first meet in an incident review. We harden them from daily practice, not from a checklist.
Secret and credential exposure
A coding agent reads whatever it can reach: .env files, private keys, database connection strings. That context travels to external inference APIs with every prompt. Without explicit exclusion rules, secrets leave your network as a side effect of normal work.
Prompt injection via files
Anyone who can land a file in your repository can embed instructions the agent follows the moment it reads them. Third-party packages, client-submitted code, public READMEs and pull requests are all live injection vectors, no compromised account required.
Auto-approved dangerous actions
Auto-approval modes execute shell commands, file edits and network calls on a heuristic guess about risk, with no human in the loop. One misclassified command with production credentials behind it is enough for irreversible damage.
What we check across your AI development workflow.
The engagement covers every layer of your AI-assisted development workflow, from the first audit through ongoing monitoring.
Most teams cannot say what their AI coding agent is allowed to touch. We map exactly what it can reach: files, environment variables, shell commands, external APIs and connected servers, so you see the whole attack surface before anything is hardened.
Audit, harden, monitor
Audit
We review your full setup: configuration files and hooks, ignore-rule coverage and file access scope, shell permissions, MCP server inventory and token storage, secrets exposure and developer practices. You receive a written findings report with severity ratings, 3 to 5 days from kickoff.
Harden
We implement the fixes: exclusion rules scoped to your secrets, command allowlists, approval gates, MCP server scoping and network isolation, plus CI checks on AI-generated pull requests. Documented and version-controlled in your repos, 5 to 7 days.
Monitor
An optional retainer: monthly configuration drift reviews, alerts on policy violations, training refreshers as your toolchain evolves, and an incident playbook your team can run without calling us first. Honest about when you no longer need it.
What we check your setup against
Publicly documented classes of AI coding agent vulnerability, mapped to the OWASP agentic risk list where it applies. The audit tests whether your configuration is exposed to each one.
Hook-based command injection. Lifecycle hooks that fire shell commands when a project opens are a publicly documented remote code execution vector. We check that every hook is scoped, reviewed and unable to run unvetted commands.
Environment variable exfiltration. One overridden variable can silently redirect authenticated API traffic to an attacker endpoint before any consent prompt appears. We check variable handling and outbound network rules.
MCP token exposure. Some MCP integrations store OAuth tokens in plaintext on the developer machine, readable by any compromised package or install script. We check where your tokens live and what can read them.
Prompt injection and goal hijacking. The top-ranked agentic risk in the OWASP list: content the agent reads, not the developer, decides what it does next. We check whether untrusted files, comments or third-party code can steer tool calls.
Sandbox escape. Agents that can modify their own execution environment have, in documented cases, disabled the very sandbox restricting them. We check that tool calls stay isolated from the host filesystem and network by default.
Supply chain and plugin risk. Malicious packages and unvetted MCP servers can rewrite local configuration to reroute authenticated traffic to attacker infrastructure. We check your dependency and plugin vetting process against this exact pattern.
Working configurations from day one, not a slide deck.
Every engagement ends with hardened, documented configuration your team uses immediately, plus the findings report behind it.
The deliverables
- Written security audit report with severity-rated findings
- Hardened ignore rules scoped to your codebase and secrets
- Shell command allowlist and approval gate policy
- MCP server trust register with per-server permission scopes
And the follow-through
- CI checks on AI-generated code before merge
- Developer runbook: injection, context hygiene, credentials
- Half-day team training workshop
- Incident response playbook, drilled once with your team
Relevant if your team uses these tools.
Claude Code is the environment we operate every day, so that is where the audit goes deepest. The same review extends across the rest of your AI coding stack.
Primary focus
- Claude Code: configuration files, hooks and permission scopes, the stack we run all day ourselves
- MCP servers: trust register, vetting and per-server permission scoping for any integration
Also covered
- Cursor: rules files, ignore coverage and context window hygiene
- GitHub Copilot: permission scoping and enterprise policy configuration
- Internal coding agents: architecture review of the assistants you built yourselves
The stack behind every audit we run
We do not reinvent the stack for every project. Every engagement runs on the same proven set of tools, swapped only where your systems require it.
Claude Code
The reasoning model behind agent decisions and tool calls
Anthropic
Managed agent runtime for long-running, autonomous tasks
MCP
The protocol that connects agents to your APIs and tools
Cursor
IDE pair-programming where the engineer reviews every diff
n8n
Workflow orchestration for cron jobs, webhooks and handoffs
Supabase
The data layer for agent memory, logs and private retrieval
Vercel
Deployment and hosting for agent-facing apps and dashboards
GitHub
Version control and review for every agent we ship
Everything runs in your accounts and your repositories. Self-hosted where data control matters. [Engagements précis à valider]
Get your questions answered
Get answers to common questions about our automation process, pricing, and results.
It is a powerful tool with permissive defaults: it can read any file it can access, execute shell commands and send context to an external API. For a team, those defaults need hardening before they become policy.
See what your team
can hand to AI
A free 30-minute call. We map one workflow together and you leave with a plan you can use, with or without us. Meeting us at the AI Summit Barcelona on September 22? Grab your slot here.
Book a Free Strategy CallRecommended services