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TARS – Open-source tool for automating pentesting with AI agents

Hacker News

TARS – Open-source tool for automating pentesting with AI agents

Hi everyone, With the rise of AI agents and recent advancements in agentic research, my co-founder and I wanted to explore whether we could develop a tool to automate parts of penetration testing. This led to the creation of TARS a few months ago. TARS is our attempt to automate parts of cybersecurity penetration testing using AI agents. Currently, we leverage CrewAI as our agentic framework and GPT-4 as our primary model. We've integrated tools that allow TARS to utilize Nettacker, RustScan, ZAP, Nmap, and web browsing via the Brave API & Selenium. Recently, we decided to make TARS accessible to everyone without any restrictions, so we open-sourced TARS under the MIT license. We see the value in TARS's ability to generate general reports from surface-level scans and its potential to perform unique attacks based on the model you're using. Since TARS is in early development and still experimental, we'd greatly appreciate your feedback. You can check out TARS here: https://github.com/osgil-defense/TARS And view some demos of TARS here: - Short Demo: https://www.youtube.com/watch?v=Sjw_gkSz6Lw - Long Demo: https://www.youtube.com/watch?v=JSBVHl7PWek Thank you for reading!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

Incorrect prediction on native model

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