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PromptShield LLMPrompt Injection Defense
Production grade prompt injection defense middleware for LLM
PromptShield sits between your users and your LLM, blocking prompt injection attacks before they reach the model. 4 layers of defense: 1. Input Classifier – detects malicious patterns 2. Context Sanitizer – strips injected instructions 3. Prompt Integrity Checker – validates structure 4. Output Monitor – catches successful injections Tunable aggression, API auth, rate limiting, audit logs, Python SDK, Docker. Benchmark tested. Built by a CS student from Nairobi, Kenya.
Hey Product Hunt! 👋
I'm Justin, a CS student from Nairobi, Kenya.
I built PromptShield because I noticed something scary —
companies are deploying LLMs in production with zero
protection against prompt injection attacks.
Attackers can hijack your AI, leak data, and bypass
your safeguards with a single malicious input.
So I built a middleware that intercepts every user input
before it touches the model and runs it through 4 defense
layers to catch and block attacks in real time.
The hardest part was getting zero false positives while
still catching 100% of attacks in benchmarks —
that balance took a lot of iteration.
Would love feedback from the community — especially
around edge cases and evasion techniques.
What would you try to break first? 🛡️
The four-layer setup sounds solid, especially the output monitor since most tools skip that. One thing that would make this way more useful for teams: a rule sharing format or community-maintained blocklist for common injection payloads. Right now every company rediscovers the same patterns. A GitHub repo where users PR new attack signatures and you ship them as versioned feeds would save everyone time and make the classifier sharper with real-world data instead of just synthetic benchmarks.
About PromptShield LLMPrompt Injection Defense on Product Hunt
“Production grade prompt injection defense middleware for LLM”
PromptShield LLMPrompt Injection Defense was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #68 on the daily leaderboard. PromptShield sits between your users and your LLM, blocking prompt injection attacks before they reach the model. 4 layers of defense: 1. Input Classifier – detects malicious patterns 2. Context Sanitizer – strips injected instructions 3. Prompt Integrity Checker – validates structure 4. Output Monitor – catches successful injections Tunable aggression, API auth, rate limiting, audit logs, Python SDK, Docker. Benchmark tested. Built by a CS student from Nairobi, Kenya.
PromptShield LLMPrompt Injection Defense was featured in Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 208.1k products, making this a competitive space to launch in.
Who hunted PromptShield LLMPrompt Injection Defense?
PromptShield LLMPrompt Injection Defense was hunted by Justin Noel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Justin Noel