LV

LVE – The First Open Repository of LLM Vulnerabilities and Exposures

Hacker News

LVE – The First Open Repository of LLM Vulnerabilities and Exposures

Hello HN, we are a team of researchers and students who have created LVE, an open source database to document and track exploits and safety issues with LLMs like (Chat)GPT, Llama and Mistral models. Our goal is to improve the discourse around LLM safety by precisely documenting, tracking LLM failures, using an open database and a small framework focused on reproducibility and traceability. LVE is meant to to raise awareness and help everyone better understand the capabilities but also the vulnerabilities of state-of-the-art large language models. Our website, lve-project.org, also hosts a series of community challenges, which are mini competitions, where you can learn about LLM safety and contribute to the project, by submitting prompts that break model behavior. We are open to feedback and suggestions, so please let us know what you think. We are also looking for contributors, so if you are interested in helping out, please reach out to us. Everything lives on GitHub at https://github.com/lve-org/lve and contribution works via PRs. Happy to answer any questions you might have!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
96%96% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, llama, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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