T3MP3ST
T3MP3ST is an open-source multi-agent offensive-security harness that turns the AI coding agent you already run into an autonomous vulnerability hunter.
If you already run Claude Code or Codex and want a red-teaming harness without a new API key or a second bill, T3MP3ST is worth the setup time. The npm run verify-claims command re-derives 27/27 benchmark checks from committed data, and 90.1% pass@1 on XBOW's 104-challenge suite sits above XBOW's self-reported 85% — you can check the numbers instead of trusting a landing page. Commercial offensive-security platforms bill per seat and keep their benchmarks behind an NDA; T3MP3ST rewards engineers who want receipts.
Verified 4d ago · liveness 73/100 · cite: rightaichoice.com/tools/t3mp3st
- Red-team engineers running continuous authorized assessments on web apps and CTFs
- AI safety researchers who need reproducible, self-verifiable benchmark harnesses
- Security teams wanting a free, self-hosted alternative to commercial offensive-security platforms like XBOW
- Developers already signed into a coding agent (Claude Code, Codex) who want an attack harness without new API keys
- Non-technical users who need a managed scanner with a GUI and a support contract
- Organizations without in-house Docker skills or anyone to interpret exploit output
- Teams that need a vendor SLA or commercial indemnity — this ships under AGPL-3.0 as-is
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
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Skip T3MP3ST if you need a managed scanner with a vendor SLA and someone else on the hook for interpreting findings, or if your engagement depends on live cloud or mobile exploitation rather than static detection.
You pay no license fee, but you supply the GPU or the model bill: running the harness offline means hosting Ollama, LM Studio or vLLM yourself, and pairing it with a hosted coding agent means paying that vendor directly.
T3MP3ST is free under AGPL-3.0 with no seat cost, no per-target fee and no cloud tenant — you self-host it. That puts it below per-seat commercial offensive-security platforms like XBOW, which bill per user and gate their benchmarks. The trade is that your 'price' becomes engineering time: Docker deployment, model inference (local or via your existing coding-agent subscription) and analyst hours to triage findings. It fits funded red teams and AI-safety labs with spare engineering capacity, not
In short
T3MP3ST — T3MP3ST is an open-source multi-agent offensive-security harness that turns the AI coding agent you already run into an autonomous vulnerability hunter. Best for Red-team engineers running continuous authorized assessments on web apps and CTFs, AI safety researchers who need reproducible, self-verifiable benchmark harnesses, Security teams wanting a free, self-hosted alternative to commercial offensive-security platforms like XBOW. Free to use.
What people actually say about T3MP3ST — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
31 mentions across 4 sources (Hacker News, Bluesky, GitHub, Lemmy) · researched Jul 16, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Autonomous multi-agent orchestration reduces manual oversight for red-teaming.
- +Modular plugin architecture enables custom attack strategies and extensibility.
- +Free, open-source, and self-hosted with no cloud dependency.
- +Keyless operation using existing AI coding agents like Claude Code.
- +Supports prompt injection, jailbreak, and adversarial testing.
- −CLI detection fails for common tools like Claude Code and Codex CLI.
- −Setting up local LLM (e.g., Ollama) is poorly documented.
- −No guidance for adding authentication headers in web scans.
- −Essential documentation for basic configuration is missing or confusing.
- −Hype exceeds proven reliability—few in-depth user reviews exist.
- • Potential cloud API costs if using remote LLMs (e.g., OpenAI API key)
- • Time investment in troubleshooting setup and configuration
Viability Score
How well maintained and how widely used is T3MP3ST? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Multi-agent orchestration for autonomous kill chains (recon → exploit → report)
- Browser-based War Room interface for driving engagements
- CLI interface for command-line control
- Keyless integration with Claude Code, Codex, Hermes, OpenCode, Oh My Pi
- Offline model support via Ollama, LM Studio, vLLM
- Black-box web app recon-to-exploit (XBEN suite, 90.1% pass@1)
- Hint-free, sandbox-jailed CTF solves (Cybench)
- Coordinated-disclosure pipeline for robotics/OT/embedded OSS (OSV + live-PoC + refuter agent)
- White-box source code analysis with blind master-builder decomposition
- Multi-language source ingest via web-tree-sitter
- Smart contract vulnerability reproduction (Damn Vulnerable DeFi)
- Cloud IaC misconfig detection benchmark (cloud:bench) with opt-in aws/az/gcloud arsenal
- Mobile static analyzer for manifest misconfigs and secret/cleartext detection (mobile:bench)
- Binary reverse-engineering sink detector (unsafe-copy, format-string, cmd-injection, int-overflow)
- Reproducible benchmark verification via npm run verify-claims (27/27 green)
About T3MP3ST
T3MP3ST is a self-hosted, open-source (AGPL-3.0) offensive-security meta-harness that repurposes the AI coding agent already on your machine — Claude Code, Codex, Hermes, OpenCode, or Oh My Pi — into an autonomous vulnerability hunter. Point it at an authorized target and the kill chain runs itself: recon → exploit → report, driven from a browser War Room or the CLI. No new API keys, no cloud tenant, no second bill. If you would rather not use a hosted agent, you can run the whole thing offline on a local model via Ollama, LM Studio, or vLLM. It is modular rather than a single scanner. Plugins cover black-box web app recon-to-exploit (XBEN suite), hint-free CTF solves (Cybench), a coordinated-disclosure pipeline for robotics/OT/embedded OSS (OSV plus live-PoC and a refuter agent), white-box source analysis with multi-language ingest via web-tree-sitter, smart contract reproduction (Damn Vulnerable DeFi), cloud IaC misconfig scaffolding (cloud:bench, with an opt-in aws/az/gcloud arsenal and tools like scoutsuite, cloudfox and pmapper), mobile static analysis for manifest misconfigs and secret/cleartext detection, and binary reverse engineering that flags sinks like unsafe-copy, format-string, command-injection and integer-overflow. Reproducibility is the stated core. Every benchmark number in the README recomputes from committed data with one command — npm run verify-claims, 27/27 checks green. On XBOW's own 104-challenge suite T3MP3ST scores 90.1% pass@1, above XBOW's self-reported 85%, alongside hint-free CTF solves and a cold hunt on real post-cutoff CVEs the model had never seen. The repo's status table is explicit about what is stable, what is experimental and what is still roadmap. Licensed AGPL-3.0 and self-hosted via Docker, it assumes real technical range: you need to read exploit output and maintain a deployment. The repo carries roughly 6.5k stars and 1.3k forks. For authorized red-team and AI-safety work it is a free alternative to per-seat commercial offensive-security platforms.
Behind the Verdict
T3MP3ST's pitch is simple and unusually well-evidenced: your AI coding agent is already capable of offensive reasoning, so the harness supplies the arsenal, the kill-chain orchestration and the reporting around it. In practice that means you install a TypeScript/Node.js project, run it under Docker or docker-compose, and it drives whichever agent you are signed into — Claude Code, Codex, Hermes, OpenCode or Oh My Pi — or a local model served through Ollama, LM Studio or vLLM. There is no cloud tenant to provision and no second API bill, which is the single biggest practical difference from commercial offensive-security platforms that charge per seat. The modular plugin set is the real substance. XBEN handles black-box web app recon-to-exploit. Cybench runs hint-free CTF solves inside a sandbox jail. An OSV-driven coordinated-disclosure pipeline pairs vulnerability database entries with a live PoC and a refuter agent — the refuter is the interesting part, because it is the mechanism that tries to disprove a finding before you file it. White-box source review ingests multiple languages through web-tree-sitter with a blind master-builder decomposition. Damn Vulnerable DeFi targets cover smart contract reproduction. cloud:bench scaffolds cloud IaC misconfig detection with an opt-in aws/az/gcloud arsenal (scoutsuite, cloudfox, pmapper). mobile:bench is a static analyzer for manifest misconfigs, exposed secrets and cleartext traffic. A binary analyzer flags classic sinks — unsafe-copy, format-string, command-injection, integer-overflow. Where it earns trust is the reproducibility discipline. Every number in the README is supposed to recompute from committed data via npm run verify-claims, reported green across 27/27 checks. That is a genuine differentiator: commercial vendors publish benchmark percentages you cannot independently re-derive, and T3MP3ST deliberately inverts that. The honest caveats are in the repo's own status table, and you should read it before committing. The domains split into proven dynamic exploitation on one side (web recon-to-exploit, CTF) and detection scaffolding on the other (cloud, mobile, binary). If your engagement needs live cloud exploitation today, T3MP3ST is not that — it is static detection and scaffolding there. It is also CLI-and-Docker shaped: no installer, no support desk, community help only, and documentation and stability can lag between commits in an actively developed repo. You must be able to read exploit output and distinguish a real finding from agent noise; a false positive pipeline that nobody adjudicates is worse than no pipeline. And authorizing scope is on you — point it only at systems you own or have explicit written permission to test.
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Real-world workflow fit
Concrete scenarios for the personas T3MP3ST actually fits — and what changes day-one when you adopt it.
You have written authorization to assess a staging web app. You start T3MP3ST from the CLI against the authorized host, let the multi-agent kill chain run recon → exploit → report, and review candidate findings in the browser War Room before writing up.
Outcome: A prioritized list of candidate vulnerabilities with reproduction steps you can adjudicate, produced without provisioning a new cloud account or buying a seat.
You want to check an offensive-security vendor's published benchmark claim. You clone the repo, run npm run verify-claims on the committed data, and re-derive the suite results, including the 90.1% pass@1 figure on the 104-challenge XBEN set.
Outcome: You either reproduce the 27/27 green checks yourself or you don't — the claim is falsifiable on your own machine rather than taken on trust.
An OSV entry lands for a dependency you maintain. You run the coordinated-disclosure pipeline, which pairs the OSV record with a live PoC and runs a refuter agent to try to disprove the finding before you act on it.
Outcome: You know whether the report is exploitable before you spend maintainer time, and you have evidence attached for a disclosure thread.
Use Cases
- Hunt for zero-day vulnerabilities in your own software before attackers do, using authorized red-team campaigns.
- Run a coordinated-disclosure pipeline against robotics, OT, or embedded OSS dependencies, pairing OSV entries with a live PoC and a refuter agent.
- Reproduce smart contract vulnerabilities against Damn Vulnerable DeFi targets as part of a review workflow.
- Analyze white-box source across multiple languages via web-tree-sitter ingest to find vulnerabilities before shipping.
- Static-analyze a mobile app build for manifest misconfigs, exposed secrets, and cleartext traffic.
- Reverse-engineer a binary to detect sink patterns such as unsafe-copy, format-string, command-injection, and integer-overflow.
- Practice CTF challenges hint-free to train a team's offensive security skills against a reproducible baseline.
- Verify vendor benchmark claims independently by re-running npm run verify-claims on committed data.
Models Under the Hood
as of 2026-10-15
Limitations
- T3MP3ST is an open-source, self-hosted offensive-security harness, so you need comfort with the command line plus a working TypeScript/Node.js environment and Docker for deployment.
- It is under active development, meaning documentation and stability can be incomplete between commits.
- Running autonomous multi-agent red-teaming demands advanced security knowledge to interpret exploit output and judge which results are real findings.
- Authorized use only: point it at systems you own or have explicit written permission to test.
as of 2026-10-13
Verification history
We have re-verified T3MP3ST 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published T3MP3ST tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (AGPL-3.0)
$0
Ideal for
Self-hosting security teams, red-team engineers and AI-safety researchers with Docker and TypeScript/Node.js skills who already run an AI coding agent or a local model server.
What this tier adds
Starting tier and only tier — $0, everything included: the full multi-agent harness, War Room and CLI, all plugins, verify-claims reproducibility harness and Docker deployment.
Where the pricing makes sense
The company stage and team size where T3MP3ST's pricing actually pencils out — and where peers do it cheaper.
T3MP3ST is free under AGPL-3.0 with no seat cost, no per-target fee and no cloud tenant — you self-host it. That puts it below per-seat commercial offensive-security platforms like XBOW, which bill per user and gate their benchmarks. The trade is that your 'price' becomes engineering time: Docker deployment, model inference (local or via your existing coding-agent subscription) and analyst hours to triage findings. It fits funded red teams and AI-safety labs with spare engineering capacity, not
Setup time & first value
How long it actually takes to get something useful out of T3MP3ST — broken out by persona, not the marketing-page minute.
Budget an afternoon, not minutes. A Docker-literate engineer can clone the repo and bring the stack up with docker-compose and a quick start pass in roughly 1–2 hours; a full authorized engagement run with a wired-up agent and a benchmark verification adds half a day. If you want the offline path via Ollama, LM Studio or vLLM, add time for model download and serving setup. Non-Docker teams should
Switching to or from T3MP3ST
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a commercial offensive-security platform (e.g. XBOW): keep the target list, re-point it at T3MP3ST's XBEN-driven recon-to-exploit plugin, and accept that you now own hosting and triage.
- →From manual Burp/ffuf-style recon: hand the same authorized scope to the multi-agent kill chain and compare its report against your own notes.
- →From a bare AI coding agent (Claude Code, Codex): install T3MP3ST around the agent you already run, so no new API keys or accounts are needed.
- →From an existing CTF training setup: move the team onto the hint-free Cybench plugin so practice runs against a reproducible baseline.
- →From a spreadsheet of dependency CVEs: feed OSV entries into the coordinated-disclosure pipeline so each one gets a live PoC and a refuter pass.
- ↗To a commercial offensive-security platform: export your findings list and re-run the same scoped targets under the vendor's managed scanner when you need a support contract behind the results.
- ↗To manual tooling (Burp, nmap, custom scripts): take T3MP3ST's reports as a starting hypothesis list and reproduce each finding by hand.
- ↗To a dedicated SAST stack: for white-box code review at scale, move the codebase into a maintained SAST product and keep T3MP3ST for adversarial validation.
- ↗To a hosted cloud-security posture tool: cloud:bench is misconfig scaffolding, so hand cloud findings to a purpose-built CSPM.
- ↗To an internal harness: the plugin architecture and committed benchmark data make it feasible to fork and maintain your own agent-orchestration layer under AGPL-3.0 terms.
Integrations
Resources & Guides
- Resourcegithub.com
T3MP3ST · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
README · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
CONTRIBUTING · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
SECURITY · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
FEATURES · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
WORKSPACE · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
WHITEPAPER · T3MP3ST
Helpful link from github.com
- Resourcegithub.com
VISION · T3MP3ST
Helpful link from github.com
Tutorials & Learning
YouTube returned 6 videos for “T3MP3ST”, and we withheld 6: 6 could not be judged, because “T3MP3ST” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about T3MP3ST.
Official links
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Mindgard
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Featured Head-to-Head Comparisons
T3mp3st vs Sublime Security
If you need to stress-test LLMs proactively and have the in-house expertise to manage open-source tooling, T3MP3ST is the free, autonomous choice. For organizations fighting targeted email threats with a need for transparent, agent-driven detection and low false positives, Sublime Security's enterprise platform delivers — but at an unknown cost and with a steeper onboarding for small teams.
T3mp3st vs Push Security
Push Security and T3MP3ST are not direct competitors—they solve different problems. If you're a security team looking to detect browser-based attacks (AiTM, session hijacking) and control employee AI tool usage with real-time policy enforcement, Push Security is the right choice. If you're an AI safety researcher or red-teamer who needs an autonomous, open-source framework to stress-test LLMs via prompt injection and jailbreak attacks, go with T3MP3ST. Pick based on your threat model: external browser attacks + AI governance vs. internal LLM robustness evaluation.
T3mp3st vs Audioeye
These tools serve completely different needs: T3MP3ST is a free, open-source red-teaming framework for LLM security researchers and red-team engineers who want autonomous adversarial testing with no cloud dependency. AudioEye is a paid, managed accessibility platform for enterprises that need rapid ADA/WCAG compliance, legal documentation, and expert support. Your choice depends on whether you're securing AI or ensuring web accessibility.
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