// I want to solve · Developers / Enterprise IT
🛡️ Let agents run safely
Give agents an isolated place to run, limit which files and networks they can touch, be able to undo mistakes, and defend against prompt injection and leaked secrets.
5 steps3 alternatives2 stacks · 6 problemschecked 2026-10-22
How to do it
- 01Give agents least privilege: start read-only and open write and network access as needed
- 02Run code in a sandbox (E2B, microsandbox, Docker + gVisor)
- 03Commit every step with git so mistakes can be rolled back
- 04Keep secrets out of prompts and repos, inject them via a secrets manager, scan with gitleaks before commits
- 05Assume prompt injection in external pages, email and documents; require human confirmation for sensitive actions
What it costs
Free start
gitleaks, gVisor, Docker are free
Pay as you go
Cloud sandboxes bill by runtime; see their sites
Team / enterprise
Enterprise secrets and audit platforms are extra
Prices change; the vendor’s official page wins. Subscriptions compared · API prices
Alternatives: which to pick
✓ Good fit
- Agents run commands, edit files or access the network
✗ Not a good fit
- Chat-only use
Ready-made stacks
All stacksSoftware that helps
All software🧱 Sandboxes & isolation🔐 Identity, secrets & agent security
Problems & solutions
All problems- How-toStop agents from deleting files or running dangerous commandsAvoiding irreversible agent mistakes takes three layers: an isolated environment, least privilege, and a checkpoint before acting.
- How-toUndo agent changes: git checkpoints, worktrees and refloggit is the most reliable undo button for agents: one branch or worktree per task, frequent commits, and reflog to recover committed states after a mistake.
- How-toKeep agents from reading .env files and committing secretsAny file an agent can read may end up in the model context, logs or even commits. Handle it on three fronts: expose less, detect early, rotate fast.
- How-toDefending agents against prompt injection from web pages and documentsThere is no complete fix for prompt injection today. In practice you treat external content as untrusted data and limit the damage an agent can do after reading it.
- How-toHow to check an MCP server is genuine and safe before installingAn MCP server runs with your permissions: you are installing a program the model can call. Look-alike package names, malicious updates and instructions hidden in tool descriptions (tool poisoning) have all happened.
- ErrorLiteLLM 1.107.0 was compromised: am I affected, and what to doOn 2026-10-21 LiteLLM’s release pipeline was compromised: all seven litellm==1.107.0 wheels on PyPI and the Docker images v1.107.0 / latest / main-latest / main-stable shipped a credential stealer and a remote-code loader. Per the official post, the PyPI package was live 11:39–13:23 UTC and the Docker images 11:46–14:20 UTC; the v1.107.0 source tag on GitHub is clean, earlier versions are safe, and the artifacts have been removed. Treat any environment that installed it unpinned or pulled those images in that window as compromised.
Needs in this scenario
-
DEV-06Destructive agent actions, permissions and sandboxing
Developers · People search for: ai agent sandbox; prevent agent rm -rf; e2b vs daytona
-
AGT-07Secret exposure, revocation and checkpoints/undo
Agent products & builders · People search for: prevent agent reading secrets; agent checkpoint undo
-
DEV-09Security: prompt injection, leaked keys, supply-chain poisoning
Developers · People search for: mcp tool poisoning; detect leaked api keys; prompt injection defense
-
AGT-09Comparable tests of sandboxes and browser automation
Agent products & builders · People search for: e2b vs modal vs daytona; browser automation for agents
-
ENT-06Agent identity, secrets and permission management
Enterprise IT · People search for: agent identity management; entra agent id; okta ai agents
Learn
All tutorials- BlueDot Impact AI Alignment 课程
BlueDot Impact · Safety & ethics · English
- BlueDot Impact AI Governance 课程
BlueDot Impact · Safety & ethics · English
- AI Safety, Ethics and Society 教材
Center for AI Safety / Dan Hendrycks · Safety & ethics · English
- MITRE ATLAS
MITRE · Safety & ethics · English
- NIST AI Risk Management Framework
NIST · Safety & ethics · English
- Lakera Gandalf / Agent Breaker 提示注入游戏
Lakera · Safety & ethics · English
- Anthropic Responsible Scaling Policy
Anthropic · Safety & ethics · English
- Google AI Principles 与负责任 AI 实践
Google AI · Safety & ethics · English
- Red Teaming LLM Applications
DeepLearning.AI / Giskard · Safety & ethics · English
- AI Alignment Forum
Alignment Forum · Safety & ethics · English