LLM Honeypot
Trap Prompt Injection and Jailbreak attacks on LLMs
What it does
I built LLM Honeypot because LLM attacks like Prompt Injection are growing fast, but defensive tools are almost non-existent. Most solutions block attackers, that teaches us nothing. A honeypot deceives them with fake credentials and logs their techniques for threat intelligence. What started as a weekend project turned into something bigger. I'd love feedback from the security and AI communities! Live: https://llm-honeypot-xmac.onrender.com GitHub: https://github.com/romiisromie/llm-honeypot
Does the same job
all alternatives →- OSOpen-source project that use LLM as deception system2025 · ▲10
Hello everyone I wanted to share a project I've been working on that I think you'll find really interesting. It's called Beelzebub, an open-source honeypot framework that uses LLMs to create incredibly realistic and dynamic deception environments. By integrating LLMs, it can mimic entire operating systems and interact with attackers in a super convincing way. Imagine an SSH honeypot where the LLM provides plausible responses to commands, even though nothing is actually executed on a real system. The goal is to keep attackers engaged for as long as possible, diverting them from your real…
- IMI made a library for LLM prompt injection/exploit/jailbreak detection2024 · github.com · ▲11
- DJDaily Jailbreak – Prompt Engineer's Wordle2025 · vaultbreak.ai · ▲130
I created a daily challenge for Prompt Engineers to build the shortest prompt to break a system prompt. You are provided the system prompt and a forbidden method the LLM was told not to invoke. Your task is to trick the model into calling the function. Shortest successful attempts will show up in the leaderboard. Give it a shot! You never know what could break an LLM.
- ARAutomated red teaming for your LLM app2024 · promptfoo.dev · ▲23
Hi HN, I built this open-source LLM red teaming tool based on my experience scaling LLMs at a big co to millions of users... and seeing all the bad things people did. How it works: - Uses an unaligned model to create toxic inputs - Runs these inputs through your app using different techniques: raw, prompt injection, and a chain-of-thought jailbreak that tries to re-frame the request to trick the LLM. - Probes a bunch of other failure cases (e.g. will your customer support bot recommend a competitor? Does it think it can process a refund when it can't? Will it leak your user's address?) -…
- LCLLMs can be susceptible to a new kind of malware2023 · github.com · ▲17
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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