yolo-cage – AI coding agents that can't exfiltrate secrets
AgentFreeI made this for myself, and it seemed like it might be useful to others. I'd love some feedback, both on the threat model and the tool itself. I hope you find it useful!Backstory: I've been using many agents in parallel as I work on a somewhat ambitious financial analysis tool. I was juggl
Capabilities6 decomposed
sandboxed-code-execution-with-secret-containment
Medium confidenceExecutes AI-generated code in an isolated sandbox environment that prevents exfiltration of secrets through network requests, file system access, or environment variable leakage. Uses OS-level process isolation (likely seccomp, AppArmor, or similar kernel-level restrictions) combined with capability-dropping to create a cage that constrains what the executed code can do while still allowing legitimate computation and file I/O within safe boundaries.
Implements kernel-level process isolation specifically designed to prevent secret exfiltration from AI-generated code, rather than generic sandboxing — uses capability-dropping and seccomp rules tuned to block credential theft vectors (environment variable access, network egress, sensitive file reads) while preserving computational legitimacy
More targeted than generic container sandboxing (Docker) because it focuses specifically on secret containment rather than full OS isolation, reducing overhead while providing stronger guarantees against credential leakage than simple process isolation
secret-filtering-and-redaction-at-execution-boundary
Medium confidenceIntercepts and filters secrets (API keys, passwords, tokens, credentials) before they can be accessed by sandboxed code execution. Likely uses pattern matching, environment variable scanning, and credential detection to identify sensitive data in the execution context, then either redacts it, blocks access, or provides a sanitized version to the executing code. Works at the boundary between the host environment and the sandbox.
Implements secret filtering at the execution boundary specifically for AI-generated code, using pattern detection and context-aware redaction rather than relying solely on runtime permissions — allows legitimate code to function while structurally preventing secret access
More proactive than traditional secret management (Vault, AWS Secrets Manager) because it actively prevents access rather than just managing rotation; more practical than full capability dropping because it allows code to run while still protecting secrets
ai-agent-code-generation-with-safety-constraints
Medium confidenceGenerates code through an AI agent (likely using an LLM like GPT-4 or Claude) that is constrained by safety guidelines and sandbox awareness. The agent understands the execution environment's limitations and generates code that respects the sandbox boundaries, avoids attempting secret access, and follows safe coding patterns. Likely uses prompt engineering, system instructions, or fine-tuning to make the agent aware of the cage constraints.
Integrates safety constraints directly into the code generation loop through agent awareness of sandbox limitations, rather than treating safety as a post-generation filter — the agent generates code that is inherently compatible with the execution cage
More efficient than post-generation code review or rewriting because constraints are baked into generation; more reliable than relying on LLM safety training alone because it uses explicit system instructions tied to the specific sandbox environment
execution-context-isolation-with-controlled-resource-access
Medium confidenceIsolates the execution context (file system, environment variables, network, system calls) for sandboxed code, providing controlled access to only necessary resources. Uses namespace isolation, chroot jails, or similar OS-level mechanisms to create a restricted view of the system. Resources are explicitly allowlisted or provided through controlled interfaces (e.g., mounted directories, injected credentials via secure channels).
Implements fine-grained resource isolation using OS-level namespaces and capability dropping, allowing precise control over what code can access while maintaining execution efficiency — goes beyond simple process isolation by controlling file system, network, and system call access
Lighter-weight than container-based isolation (Docker) because it uses kernel namespaces directly rather than full container runtime; more flexible than static allowlists because it can be configured per-execution based on code requirements
audit-logging-and-security-event-tracking
Medium confidenceLogs all execution events, access attempts, and security violations in the sandboxed environment. Tracks what code tried to do (successful and failed operations), what secrets it attempted to access, what network calls it made, and what system calls it invoked. Provides audit trails for compliance, debugging, and security analysis. Likely uses kernel-level tracing (auditd, eBPF) or runtime hooks to capture events.
Implements comprehensive audit logging specifically for sandboxed AI-generated code execution, capturing both successful operations and failed access attempts — uses kernel-level tracing to provide visibility into what code tried to do, not just what it succeeded in doing
More detailed than application-level logging because it captures system-level events that code cannot hide or suppress; more actionable than raw kernel traces because it's filtered and structured for security analysis
capability-based-access-control-for-code-operations
Medium confidenceImplements fine-grained capability-based access control where code is granted specific capabilities (e.g., 'read from /tmp', 'write to output directory', 'call specific APIs') rather than broad permissions. Uses seccomp filters, AppArmor profiles, or SELinux policies to enforce capabilities at the kernel level. Code cannot perform operations outside its granted capabilities, even if it attempts to escalate privileges or use alternative system calls.
Uses kernel-level capability-based access control (seccomp, AppArmor, SELinux) to enforce fine-grained permissions on code execution, preventing even privileged code from performing unauthorized operations — goes beyond traditional role-based access control by operating at the system call level
More secure than application-level access control because code cannot bypass kernel-level enforcement; more flexible than static allowlists because capabilities can be dynamically configured based on code requirements
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Train, fine-tune-and run inference on AI models blazing fast, at low cost, and at production scale.
open-cowork
Open-source AI agent desktop app for Windows & macOS. One-click install Claude Code, MCP tools, and Skills — with sandbox isolation, multi-model support, and Feishu/Slack integration.
Gru Sandbox
** - Gru-sandbox(gbox) is an open source project that provides a self-hostable sandbox for MCP integration or other AI agent usecases.
smolagents
🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents.
Sandbox Agent SDK – unified API for automating coding agents
We’ve been working with automating coding agents in sandboxes as of late. It’s bewildering how poorly standardized and difficult to use each agent varies between each other.We open-sourced the Sandbox Agent SDK based on tools we built internally to solve 3 problems:1. Universal agent API: interact w
Best For
- ✓Teams deploying autonomous coding agents in security-sensitive environments
- ✓Developers building internal tools that execute LLM-generated code
- ✓Organizations with strict data governance requiring proof of secret containment
- ✓CI/CD pipelines running AI-generated code with access to production secrets
- ✓Multi-tenant platforms where code isolation is critical
- ✓Development teams that want defense-in-depth against credential leakage
- ✓Autonomous coding agents that need to generate production-safe code
- ✓Teams using LLMs for code generation but concerned about security implications
Known Limitations
- ⚠Sandbox overhead adds latency to code execution (typically 50-500ms per invocation depending on kernel implementation)
- ⚠Cannot execute code requiring privileged system calls (e.g., raw socket creation, direct hardware access)
- ⚠Network isolation may break legitimate use cases requiring external API calls — requires explicit allowlisting
- ⚠Performance degrades with high-frequency execution due to sandbox setup/teardown costs
- ⚠Pattern-based detection may miss obfuscated or encoded secrets
- ⚠Requires explicit configuration of what constitutes a 'secret' — no universal standard
Requirements
Input / Output
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