🔍 Is your AI gateway really secure?
🚀 RouterBeat Security Assessment is now LIVE — 100% FREE!
🛡️ 13 specialized detection engines
📊 Comprehensive security scoring
⚠️ Explore risks like prompt injection, data exfiltration, and token inflation.
Test your AI gateway now 👇
https://t.co/zMi6xXr8mL
#AISecurity #AIGateway #RouterBeat
💬 Want to discuss your results or share feedback?
Join our AIBEAT Discord community and connect with us in the #routerbeat channel! 🚀
👉 https://t.co/m3EUg4RgNo
🔍 Is your AI gateway really secure?
🚀 RouterBeat Security Assessment is now LIVE — 100% FREE!
🛡️ 13 specialized detection engines
📊 Comprehensive security scoring
⚠️ Explore risks like prompt injection, data exfiltration, and token inflation.
Test your AI gateway now 👇
https://t.co/zMi6xXr8mL
#AISecurity #AIGateway #RouterBeat
@elonmusk When an AI agent has network access, credentials, and tools, safety also depends on what it can reach, what it can do, and whether someone outside the model can stop it. Can we build systems that are both truth-seeking and externally constrained?
@vineete_5 AI safety used to mean: Will the model refuse a dangerous request?
Now the harder question is: How far can it go once it has access to networks, credentials, and tools?
We tested a poisoned-document scenario in a CrewAI + RAG setup.
A few interesting defense ideas came up from the discussion:
• Treat retrieved content as untrusted by default
• Use structured JSON/XML instead of raw text
• Re-check tool calls against the original user task before execution
Which approach would you trust most in a real RAG / Agent system?
@aacle_ Hey Abhishek , just sent you a DM 😊
Would love to pick up where we left off and explore a collaboration with AIBEAT. Please check when you have a moment
@i_mika_el Great question! We’re still finalizing the criteria and will share the details in our Discord.
Feel free to join us here:
https://t.co/4zcBxvQPeP
AIBEAT Seed Community Co-Building Program — Coming Soon
We’re preparing a new co-building program for early contributors, with opportunities to join real-world testing, share cases, provide feedback, and contribute to AIBEAT.
Bounty activities are also coming soon, with cash rewards for valuable test cases, findings, and meaningful contributions.
Feel free to invite more builders, researchers, and security practitioners interested in LLM, RAG, Agents, AI security, red teaming, and developer tools to join us.
More details coming soon!
https://t.co/wyYJ06TWYe
@DanKornas Hi Dan — I sent you a collaboration request for AIBEAT on Passionfroot yesterday. Would love to explore a short X collaboration if you’re interested. Thanks!
Building AIBEAT — open-source AI security testing for LLMs & Agents.
We’re looking for a few early partners: AI security creators, researchers & community builders.
If you’d like to help more people discover AIBEAT, we’d love to collaborate.
DM us or email: [email protected]
Prompt attacks are only half the story.
PromptBeat creates and runs the attacks.
AgentBeat follows what the Agent actually does next — traces, environment changes, and evidence.
That’s AIBeat in 35 seconds.
A poisoned document entered a CrewAI RAG pipeline.
The Agent repeated the planted instruction — and cited it.
Fail already, or only when it acts?
Where would you draw the line?