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Sandboxing untrusted code using WebAssembly

Details

External ID
46871387
Source
HN
Company
—
Product
Sandboxing untrusted code using WebAssembly
Website domain
github.com
Launched
Feb. 3, 2026
Cohort
—
Upvotes
76
Upvotes percentile
0.8638814016172507
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Hi everyone,I built a runtime to isolate untrusted code using wasm sandboxes.Basically, it protects your host system from problems that untrusted code can cause. We’ve had a great discussion about sandboxing in Python lately that elaborates a bit more on the problem [1]. In TypeScript, wasm integration is even more natural thanks to the close proximity between both ecosystems.The core is built in Rust. On top of that, I use WASI 0.2 via wasmtime and the component model, along with custom SDKs that keep things as idiomatic as possible.For example, in Python we have a simple decorator: from capsule import task @task( name="analyze_data", compute="MEDIUM", ram="512mb", allowed_files=["./authorized-folder/"], timeout="30s", max_retries=1 ) def analyze_data(dataset: list) -> dict: """Process data in an isolated, resource-controlled environment.""" # Your code runs safely in a Wasm sandbox return {"processed": len(dataset), "status": "complete"} And in TypeScript we have a wrapper: import { task } from "@capsule-run/sdk" export const analyze = task({ name: "analyzeData", compute: "MEDIUM", ram: "512mb", allowedFiles: ["./authorized-folder/"], timeout: 30000, maxRetries: 1 }, (dataset: number[]) => { return {processed: dataset.length, status: "complete"} }); You can set CPU (with compute), memory, filesystem access, and retries to keep precise control over your tasks.It's still quite early, but I'd love feedback. I’ll be around to answer questions.GitHub: https://github.com/mavdol/capsule[1] https://news.ycombinator.com/item?id=46500510

Enrichment

Theme
self-hosted infrastructure and security tools
Vertical
Security
Function
Dev tools
Audience
Developer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
sandboxing code with webassembly
Manually corrected
False

Could you build this?

Partial Writing a wrapper around an existing Wasm engine is straightforward, but creating a secure sandbox that isolates untrusted code requires rigorous security boundary engineering and low-level runtime integration.

What it would actually take: This requires embedding a WebAssembly runtime (such as Wasmtime, Wasmer, or V8) into a host language (TypeScript/Node.js or Rust) and defining strict WASI capabilities, memory limits, CPU cycle fuel metering, and secure hostcall bindings. The primary difficulty is hardening the sandbox against side-channel attacks, escape vulnerabilities, and CPU/memory exhaustion while ensuring near-native performance for dynamic untrusted scripts. It requires systems programmers with deep expertise in WASI specifications and runtime isolation security.

Discussion

20 comments analyzed.

Competitors mentioned: just-bash (for coding agent execution), Modal (for remote infrastructure decorator pattern), Pyodide (for Python in WebAssembly with C extensions), Mruby (for sandboxed VM with limited functionality)

Concerns raised: Unclear how duck-typed languages like Python work with WASM component model IDL, Decorator syntax confusing without clear understanding of what runs where, No network limits to prevent resource exhaustion loops or massive downloads, WASI tooling maturity and dynamic linking not yet at Emscripten/Pyodide level, Unclear whether agents run fully in sandbox or if code is extracted and run dynamically

Feature requests: More complete examples showing AI agent integration patterns, Network request limits and rate limiting controls, Option to run sandbox code as separate file for transparency, Visual/debugging tools to see which code runs directly vs. in sandbox, Support for Python libraries like Pandas in WebAssembly

Competitors

Other products that read as similar to this one — 179 launches clear the similarity bar, closest 8 shown.

Attention rank: #29 of 180 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 94 days after the earliest competitor.

Other launches for this product

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