This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Most AI agent tests check if your agent gave the right answer. Roleplay tests whether it can be manipulated into doing the wrong thing. Roleplay runs social-engineering attack packs against your agent, captures exploit proof, helps verify the fix, and keeps checking for regressions, so agent security becomes a repeatable workflow, not a one-time vibe check. Try it on roleplay.sh.
Hi everyone 👋 I’m Ibrahim, the founder of @Roleplay.
The idea started from a simple question: If AI agents are starting to act like humans, and humans can be manipulated, can AI agents be manipulated into doing what they’re not supposed to do?
A lot of AI agent testing focuses on correctness, prompt injection, or one-time evals. Those matter, but they don’t fully answer what happens when an agent is pressured, persuaded, or tricked inside a real workflow.
That’s where Roleplay comes in.
Roleplay tests whether your AI agent can be socially engineered into approving the wrong action, revealing sensitive information, bypassing a policy, trusting fake authority, or misusing a tool.
It runs social-engineering attack packs against your agent, captures exploit proof, helps verify fixes, and keeps checking that the same failure doesn’t come back.
The current version includes:
Attack packs for fake authority, urgency pressure, policy bypass, data extraction, and tool misuse
Specialized packs for customer relationship, sales/SDR, and recruiting/HR agents
Local testing with the included CLI runner
Sanitized evidence uploads
Exploit proof and replay
Fix verification
Scheduled monitoring
Regression gates
Agent Risk Profile
I’d really appreciate your feedback, especially if you’re building, deploying, or securing AI agents.
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About Roleplay on Product Hunt
“Social-engineering tests for AI agents”
Roleplay was submitted on Product Hunt and earned 13 upvotes and 1 comments, placing #39 on the daily leaderboard. Most AI agent tests check if your agent gave the right answer. Roleplay tests whether it can be manipulated into doing the wrong thing. Roleplay runs social-engineering attack packs against your agent, captures exploit proof, helps verify the fix, and keeps checking for regressions, so agent security becomes a repeatable workflow, not a one-time vibe check. Try it on roleplay.sh.
Roleplay was featured in Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers) and Security (2.7k followers) on Product Hunt. Together, these topics include over 185.6k products, making this a competitive space to launch in.
Who hunted Roleplay?
Roleplay was hunted by Ibrahim Cheurfa. 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.
Want to see how Roleplay stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ibrahim Cheurfa
Hi everyone 👋 I’m Ibrahim, the founder of @Roleplay.
The idea started from a simple question: If AI agents are starting to act like humans, and humans can be manipulated, can AI agents be manipulated into doing what they’re not supposed to do?
A lot of AI agent testing focuses on correctness, prompt injection, or one-time evals. Those matter, but they don’t fully answer what happens when an agent is pressured, persuaded, or tricked inside a real workflow.
That’s where Roleplay comes in.
Roleplay tests whether your AI agent can be socially engineered into approving the wrong action, revealing sensitive information, bypassing a policy, trusting fake authority, or misusing a tool.
It runs social-engineering attack packs against your agent, captures exploit proof, helps verify fixes, and keeps checking that the same failure doesn’t come back.
The current version includes:
Attack packs for fake authority, urgency pressure, policy bypass, data extraction, and tool misuse
Specialized packs for customer relationship, sales/SDR, and recruiting/HR agents
Local testing with the included CLI runner
Sanitized evidence uploads
Exploit proof and replay
Fix verification
Scheduled monitoring
Regression gates
Agent Risk Profile
I’d really appreciate your feedback, especially if you’re building, deploying, or securing AI agents.