Derusted – An open-source programmable HTTPS MitM proxy engine in Rust

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I've released Derusted — a programmable HTTPS MITM proxy engine written in Rust. This grew out of frustration with existing MITM and proxy tooling being: - unsafe or outdated - coupled to one runtime or protocol - hard to embed into other projects - not flexible for security/compliance use cases Derusted is a library-first design, meant to be used inside other systems like: - browser automation tooling - secure proxies and gateway stacks - traffic inspection - network research - observability and incident response tooling Highlights: - Written fully in safe Rust - Supports HTTP/1.1 & HTTP/2 MITM - Pluggable inspection pipeline - Certificate generation + pinned cert detection - Redaction support for sensitive data - No `unsafe` - ~150 tests Links: Repo: https://github.com/kumarimlab/derusted Crate: https://crates.io/crates/derusted Docs: https://docs.rs/derusted/latest/derusted/ Still early, but I'd love feedback — especially around QUIC/H3, benchmarking, use cases, and potential improvements. Happy to answer questions.

Visit product View on Hacker NewsNov 27, 2025

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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