Open-Source Security · afshinm

zerobox

Zerobox is a lightweight process sandbox for Rust, TypeScript, and Python that isolates command execution with fine-grained controls over file access, network, and environment variables. It uses native OS primitives (Seatbelt on macOS, Bubblewrap on Linux) rather than containers, adding ~10ms overhead while allowing safe execution of untrusted code—particularly useful for AI agents and LLM tool calls.

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Source: GitHub — github.com/afshinm/zerobox

669

GitHub stars

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	afshinm/zerobox
Owner	afshinm
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	669
Forks	39
Open issues	8
Latest release	v0.3.3 (2026-05-17)
Last updated	2026-05-17
Source	https://github.com/afshinm/zerobox

What zerobox is

Built in Rust, Zerobox wraps subprocess execution with OS-level sandboxing primitives and a network proxy layer. It enforces deny-by-default policies for writes, network, and environment vars; injects secrets only at network interception points so sandboxed processes never see real credentials; and provides SDKs across three languages with a consistent API.

Quickstart

Get the zerobox source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/afshinm/zerobox.git2cd zerobox3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

AI agent and LLM tool isolation

Sandbox individual tool calls or entire agent workflows to prevent data exfiltration, file corruption, or unintended network access when executing AI-generated or third-party code.

Safe execution of untrusted scripts

Run downloaded scripts, build artifacts, or user-submitted code with strict file and network boundaries, rolling back changes post-execution if needed via snapshot/restore.

Credential injection for API integrations

Pass API keys to sandboxed processes without exposing them; secrets remain hidden in environment and are swapped only at the proxy level for approved domains.

Implementation considerations

Verify OS support: macOS and Linux only; plan Windows migration or alternative solution.
Node.js fetch does not respect HTTPS_PROXY by default; use --use-env-proxy flag when relying on secret injection in Node environments.
Secret injection is domain-scoped; clarify host allowlists upfront to avoid over-permissioning or missed network proxying.
Snapshot/restore feature adds I/O overhead for large file operations; profile for performance-critical workloads.
Sandbox rules (file paths, domains) are process-level; no role-based or time-based policy management built in.

When to avoid it — and what to weigh

Windows-only deployments — Windows support is marked as planned; production use is currently limited to macOS (Seatbelt) and Linux (Bubblewrap + Seccomp).
Requiring kernel-level isolation guarantees — Zerobox uses OS sandboxing features, not full VM isolation; it is not a replacement for hypervisor-based sandboxes if you need absolute process isolation guarantees.
Long-lived background processes — Designed for transient command execution (scripts, builds, tool calls); suitability for persistent daemon processes or long-running services is not documented.
Complex multi-process orchestration — Focuses on single-command sandboxing; coordinating sandboxes across many interdependent processes or services requires additional architecture.

License & commercial use

Apache License 2.0 (Apache-2.0). This is a permissive OSI-approved license that allows commercial use, modification, and distribution with attribution and legal liability disclaimers.

Apache-2.0 permits commercial use without royalty. However, ensure your use case aligns with the license terms (attribution, liability disclaimers). For enterprise deployment, audit integration points and security properties with your legal/infosec teams; no explicit commercial support model is stated in the available data.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Zerobox relies on OS-level sandboxing (Seatbelt/Bubblewrap). Threat model and attack surface (e.g., privilege escalation, escape vectors, proxy bypass) are not formally documented. Secret injection at the proxy layer reduces credential exposure but introduces a local proxy as a potential attack point. No third-party security audit or CVE history provided. Evaluate sandbox effectiveness for your specific threat model (e.g., untrusted code from AI models vs. verified but isolated builds). Test sandbox escape scenarios in your environment.

Alternatives to consider

Docker / containerd

Full container isolation with broader ecosystem, but heavier (~100MB+), requires daemon, and overkill for simple script isolation. Better for multi-process or persistent workloads.

gVisor / Kata Containers

Stronger kernel-level isolation than OS primitives, but higher overhead and complexity. Suitable for high-trust-boundary scenarios.

Native OS sandboxing (Pledge/Unveil on OpenBSD, pledge on Linux)

Lower-level alternatives with finer control, but less portable and more complex to use; Zerobox abstracts these with a unified API.

Software development agency

Build on zerobox with DEV.co software developers

Zerobox is ideal for safely running AI-generated code, untrusted scripts, and isolated tool calls. Test it on macOS or Linux, review the security model, and assess fit for your deployment constraints. Consider alternatives if you require Windows support or kernel-level isolation guarantees.

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zerobox FAQ

Can I run Zerobox on Windows?

Not yet. Windows support is marked as planned. Currently, only macOS (Seatbelt) and Linux (Bubblewrap + Seccomp) are fully supported.

How much does Zerobox slow down execution?

Typical overhead is ~10ms per command, with memory overhead around 7MB. Performance varies by workload; run ./bench/run.sh to profile your use case.

Can a sandboxed process see the real API key or token?

No. When using --secret and --secret-host, the sandboxed process sees a placeholder (ZEROBOX_SECRET_...). The real credential is substituted only at the network proxy level, and only for approved domains.

Is Zerobox suitable for production AI agent deployments?

It is designed for this use case, but review the security model and test sandbox escape scenarios in your environment. Single-maintainer, young project (~2 months old); assess your risk tolerance and availability requirements.

Custom software development services

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