AI coding agents like Claude Code, Cursor, or Copilot need real access to do their job — they read your project files, run commands in your terminal, sometimes see your screen. The problem is that "your project files" often includes a .env full of live API keys, and "your terminal" can end up echoing a secret straight into the agent's context window, where it might get logged, sent to a model provider, or accidentally pasted into a bug report. None of these tools try to stop your agent from working — they narrow the gap between "the agent can use this credential" and "the agent can actually read this credential," which is a much smaller, much safer thing to allow.
We pulled together six real, working tools that each solve one slice of this problem — from keeping API keys out of an agent's memory entirely to catching a secret before it leaks through a screenshot. Most are young, small open-source projects (some with fewer than 20 GitHub stars at the time of writing), so treat them as promising rather than battle-tested, but they're real, live code you can run today.
1. AgentSecrets — the key never enters the agent's memory
AgentSecrets is a free proxy that lets an AI agent make authenticated API calls without the real key ever touching its memory: the credential stays in your OS keychain, and the proxy injects it at the transport layer using one of six auth styles (bearer, header, query, basic, JSON, form). It ships ready-made MCP servers for Claude and Cursor, and logs every use with a SHA-256 audit trail encrypted via X25519 + AES-256-GCM.
Price: Free, open source (MIT).
Best for: anyone whose agent needs to call external APIs regularly and wants the key structurally unable to leak, not just "hopefully not read."
Strengths: injection happens below the agent, not in a prompt; six auth styles cover most APIs; audit log by default; ready MCP servers for Claude and Cursor.
Limits: small community project (167 stars), not backed by a company; the local proxy is one more moving part to debug; marketing site was unstable when we checked.
2. Enject — your .env file, without the real values
Enject takes a narrower, very specific approach: it swaps the actual secrets in your .env file for harmless placeholders (like en://STRIPE_KEY) on disk, so when your AI coding assistant reads your project files, it never sees a real password or API key in the first place. Secrets are encrypted with AES-256-GCM + Argon2id and only resolved back to real values at the point your app actually runs.
Price: Free, open source (MIT).
Best for: teams whose main leak vector is simply "the agent reads the repo, including the .env file."
Strengths: simple, symbolic-reference model that's easy to reason about; secrets never sit in plaintext on disk; 501 GitHub stars — real adoption for a precise problem; author is upfront about its limits.
Limits: still in alpha (v0.2.0), so expect breaking changes; only covers the .env vector, not a full secrets manager; can't stop an agent that deliberately writes exfiltration code.
3. Secretctl — a secrets manager built for the AI-coding era
Secretctl is a local-first secrets manager designed specifically so tools like Claude Code can use your API keys without the plaintext value ever entering the agent's context. It integrates natively with MCP, encrypts everything with AES-256-GCM + Argon2id, and keeps a tamper-evident, HMAC-chained audit log — all with no cloud dependency, as a single cross-platform binary with both CLI and desktop apps.
Price: Free, open source (Apache 2.0).
Best for: developers who want one general-purpose, local secrets manager built around MCP rather than a point fix for one leak vector.
Strengths: native MCP integration; secrets never enter the agent's context at all; tamper-evident audit log; works as CLI or desktop app, no cloud required.
Limits: very young, single-maintainer project with limited GitHub traction so far; not meant as a general password manager; not yet proven at scale.
4. Privacy Mask — catches what you're about to paste into a chat
Privacy Mask attacks a different leak vector entirely: the screenshot you're about to paste into an AI chat to explain a bug. It runs two OCR engines (Tesseract + RapidOCR) locally to detect and blur 40+ types of sensitive data — phone numbers, ID cards, API keys embedded as text in an image — before you share it, and integrates with 20+ AI coding platforms. Processing is 100% local and offline, so the original image never leaves your machine.
Price: Free, open source (MIT).
Best for: anyone who regularly screenshots their screen (including terminals or admin panels) to show an AI assistant a problem.
Strengths: fully local processing, nothing uploaded; dual OCR engine catches text baked into images, not just typed text; broad platform integration; detects a wide range of sensitive data types.
Limits: young, low-visibility project (11 stars); automatic detection is never guaranteed to catch everything; critical cases still need a manual check before you hit paste.
5. FireClaw — strips hidden attacks out of what the agent reads
FireClaw comes at this from the opposite direction: instead of protecting your secrets from leaking out, it protects your agent from malicious instructions hidden in the pages it browses. It runs web content through a four-stage defense pipeline before the agent ever sees it, with a "no bypass" mode that can't be accidentally disabled, DNS blacklists, canary-token detection, and a full JSONL audit log.
Price: Free, open source (AGPLv3), donations optional.
Best for: teams giving an agent real web-browsing ability and worried about prompt injection hidden in pages it visits.
Strengths: four-stage defense pipeline; protection can't be silently turned off; canary tokens plus DNS blacklisting; full audit trail in JSONL.
Limits: young project (17 stars, 1 fork); the injection-pattern database is still maturing; adds proxy latency to the agent's browsing pipeline.
6. TokenTimer — makes sure nothing expires silently
TokenTimer covers a different but related risk: secrets that quietly expire. It's an open-source tracker that discovers certificates, API tokens, secrets, and licenses across your systems, alerts before they lapse, and can trigger automated renewal where the underlying system supports it — storing only metadata, never the actual secret values.
Price: Self-hosted open source is free; a cloud-managed SaaS version exists with unpublished pricing.
Best for: teams that have already locked down how agents access secrets and now want to avoid the more mundane failure mode of a token silently expiring in production.
Strengths: automatic discovery of certs, tokens, secrets, and licenses; metadata-only storage, never the sensitive value itself; automated renewal where possible; open source, self-host or cloud.
Limits: SaaS pricing isn't published; not a replacement for a real secrets manager like Vault; automated renewal only works where the underlying system allows it.
| Tool | What it protects | Price | Best for |
|---|---|---|---|
| AgentSecrets | API keys during agent calls | Free, open source | Agents that call external APIs |
| Enject | .env secrets on disk | Free, open source | Repos with real secrets in .env |
| Secretctl | General secrets via MCP | Free, open source | MCP-based secrets manager, no cloud |
| Privacy Mask | PII/keys in screenshots | Free, open source | Pasting screenshots into AI chat |
| FireClaw | Prompt injection from web content | Free, open source | Agents that browse the web |
| TokenTimer | Silent cert/token expiration | Free self-hosted, SaaS TBA | Avoiding expired-secret outages |
The short version: no single tool here covers the whole problem, and that's normal for how young this space is. If you only fix one thing, start with the .env vector — Enject or Secretctl — since that's the most common way a real key ends up sitting in plain text where an agent can read it. Add AgentSecrets if your agent needs to actively call authenticated APIs, Privacy Mask if screenshots are part of your debugging workflow, and FireClaw once you let an agent browse the open web unsupervised.