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AEGIS
Open-source LLM defense that publishes its real bypass rate
AEGIS is a self-hosted, open-source defense layer for LLM apps and agents: prompt injection detection, policy enforcement, and tool/agent permissioning that stops an injected prompt from misusing a tool -- rare among open-source guardrails. What's different: we ran our detectors through adaptive red-team testing (attacks that evolve from prior bypasses) and published the honest result: hardening barely moves the number against an adaptive attacker. Apache 2.0, no signup for the live demo.
Hey Product Hunt — I built AEGIS, an open-source defense-in-depth platform that sits between your app and any LLM provider: input/output filtering, a policy engine, and (the part I haven't seen elsewhere) tool/agent permissioning with taint tracking, so a prompt injection can't quietly get an agent to misuse a tool.
The part I actually want feedback on: most guardrail projects report a round-1 catch rate against a fixed test set, which tells you almost nothing about a real attacker who adapts. I ran AEGIS's own detectors through adaptive red-team campaigns that breed new attacks from whatever got past the previous round, and published the result even though it isn't flattering: hardened detectors converge to about the same ~48% overall bypass rate as unhardened ones by round 3. Full numbers: github.com/hamidmatiny/aegis/blob/main/RESULTS.md
Try the live demo above, no signup needed — send it a benign question and a prompt injection side by side and watch the difference.
Would genuinely love feedback on: whether the adaptive red-team methodology holds up to scrutiny, what I'm missing on the agent tool-permissioning side, and whether "publish your real bypass rate" is something more guardrail tools should be held to.
About AEGIS on Product Hunt
“Open-source LLM defense that publishes its real bypass rate”
AEGIS was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #158 on the daily leaderboard. AEGIS is a self-hosted, open-source defense layer for LLM apps and agents: prompt injection detection, policy enforcement, and tool/agent permissioning that stops an injected prompt from misusing a tool -- rare among open-source guardrails. What's different: we ran our detectors through adaptive red-team testing (attacks that evolve from prior bypasses) and published the honest result: hardening barely moves the number against an adaptive attacker. Apache 2.0, no signup for the live demo.
On the analytics side, AEGIS competes within Software Engineering, Artificial Intelligence, GitHub and Development — topics that collectively have 568.3k followers on Product Hunt. The dashboard above tracks how AEGIS performed against the three products that launched closest to it on the same day.
Who hunted AEGIS?
AEGIS was hunted by Hamid Matiny. 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.
For a complete overview of AEGIS including community comment highlights and product details, visit the product overview.
Hamid Matiny