US-based AI Startup company Delivering cyber security models via Confidential Computing Inference.
San Francisco
Joined December 2024
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Real-world threat intelligence, not just benchmarks.
Security researcher Joseph Harrison (Cybersecurity OPS lead) used CyberKimi and CyberGLM in his investigation into cyber intrusions affecting Brazil's public sector.
We are proud to support cybersecurity researchers doing important defensive work.
Link to the threat intel report below written by the author :
the-hunters-ledger.com/repor…
We’re opening free access to CyberGLM at Adverserial AI.
Defenders need capable AI tools to investigate incidents, test their defenses, and respond faster. They also need confidence that sensitive customer data stays private.
Our approach brings those priorities together:
• Free access to CyberGLM.
• Customer prompts are not sold or used to train our models.
• Inference runs inside confidential virtual machines using Intel TDX.
• Open-source verification tools let you inspect our implementation and verify attestation evidence.
We build our training data through controlled lab work, real-world attack emulation, and synthetic data generation. Customer conversations are not our training pipeline.
The video explains how the infrastructure works, how verification fits in, and why we’re making this capability available.
To request free access, email [email protected] or visit adverserial.ai.
If you’re a defender, researcher, or potential partner, I’d love to hear what you’re working on.
Hello internet,
If you are a defender, we are offering our cyber capable AI models free of charge.
All you have to do is contact us here [email protected]
Our models runs in a confidential VM,There’s no logs or prompts we have access to.
Let’s give you the exact AI capabilities the attackers have, but for free.
@grok spread the word please.
Introducing ACK : Adverserial Confidential Kit.
Your AI harness. Our cybersecurity intelligence. Verifiable confidentiality.
We're introducing ACK, making it easier than ever to integrate CyberGLM into your existing AI workflows.
Compatible with:
Claude
Codex
Pi
OMP
Hermes
ZCode
DeepSeek Harness
Zero configuration. Verifiable attestation. Ready in seconds.
Our latest demo showcases multiple harness integrations, confidential computing attestation verification, and CyberGLM running seamlessly across environments.
Tired of hitting security guardrails when performing legitimate cybersecurity research?
We're building an alternative.
No need to wait for CVP access or DayBreak. Connect your preferred harness to CyberGLM and get to work.
And one combination we're particularly excited about:
Hermes + CyberGLM feels like a cheat code.
The future of cybersecurity AI should be accessible, interoperable, and verifiably private.
Explore ACK:
github.com/AdverserialAI/adv…
The future of cybersecurity isn’t coming. It’s verified.
Confidential inference in an attested TEE. No GPU request logs. No training on your prompts.
adverserial.ai
The adversary learned to think.
Now it's time for the defenders to fight back.
Adverserial AI was born from a simple conviction: the future of cybersecurity demands more than powerful AI. It demands trust that can be verified, not merely promised.
Built by a reverse engineer who spent years understanding how adversaries operate, we're developing a new generation of cybersecurity AI.
Confidential inference. Verifiable privacy. No blind trust.
Every revolution has a beginning.
This is ours.
ADVERSERIAL AI - THE BEGINNING.
Don't just take our word for it. Check the code.
At Adverserial AI, we're building verifiable privacy for cybersecurity AI, and we're making the underlying technology publicly accessible.
Explore our confidential computing infrastructure, attestation tools, SDKs, and security policies.
Read the code. Verify the claims. Build with us.
GitHub: github.com/AdverserialAI
AdverserialAI retweeted
Love seeing my kids finally realizing that you can beat Frontier in specialized tasks - you know the stuff we’ve been pushing for early this year but no one believed.
If you want to see Cyber AI models (CyberKimi) outcompeting Claude Mythos in exploit dev check this out: adverserial.ai
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Privacy shouldn't require trust. It should be cryptographically verifiable.
Our founder @lordx64 built Adverserial AI's confidential inference infrastructure so anyone can independently verify our security claims, directly from their browser.
When security researchers recognize what we're building, it means a lot.
Huge thanks to @msolnik for recognizing the work behind our infrastructure.
Don't trust. Verify.
This should be the industry standard.
verify.adverserial.ai
I need to give credit where credit is due. @adverserialAI and @lordx64’s implementation of verifiable encrypted inference is what everyone should strive for. You can verify the whole stack with a click in your browser. This should be the standard!
CyberGLM in the wild.
A security researcher just shared how they're using CyberGLM / Cyberkimi for malware analysis on Hack The Box. @hackthebox_eu
Multiple stages, sophisticated anti-analysis, complex architecture… only to uncover background ad fraud.
We love seeing the community put our models to work.
Built for cybersecurity. Used by cybersecurity professionals.
Verify, Don't Trust.
Adverserial AI Brings Confidential Computing Attestation to Claude Code.
At Adverserial AI, we're making AI privacy independently verifiable.
You can now verify our Confidential Virtual Machine (CVM) attestation directly from Claude Code using our new plugin.
Simply run:
/adverserial-verify-claude:attestation
We've also released plugins for Kimi Code, OpenAI Codex, Hermes, OpenCode, and Pi, allowing developers to verify our confidential computing infrastructure directly from their preferred AI environments.
Our public SDK also enables anyone to build custom integrations. Contributions and pull requests are welcome.
No blind trust. No empty privacy promises. Just cryptographic proof.
Explore:
GitHub: github.com/AdverserialAI/
Confidential Computing: adverserial.ai/confidential
Verify Attestations: verify.adverserial.ai
About 10 days ago, we launched CyberPvP, putting CyberKimi head-to-head against @AikidoSecurity ALTAR-1 on 100 randomly selected CyberGym challenges.
For the first 200 minutes, both models performed similarly. As the challenges became more complex, a clear performance gap began to emerge, particularly in long-horizon reasoning and agentic execution.
At one of our checkpoints, the results were:
CyberKimi vs. ALTAR-1
Challenges solved: 82/84 vs. 61/100
Iterations: 6,619 vs. 9,312
Tokens consumed: 336M vs. 475M
Tool calls: CyberKimi used approximately 3x fewer
CyberKimi was solving more challenges while consuming significantly fewer computational resources.
These were preliminary checkpoint results, with different numbers of tasks attempted, rather than a final head-to-head comparison.
What makes CyberPvP different is transparency. We publish execution traces and make the competition publicly observable so anyone can examine how the models perform, not just take our word for it.
Cybersecurity AI should be evaluated on real tasks, sustained reasoning, execution efficiency, and independently verifiable results.
See the challenge: cyberpvp.adverserial.ai/grap…
CyberGLM is back LIVE.
We temporarily took our infrastructure offline last week to perform a major upgrade to our TEE-based Zero Data Retention (ZDR) infrastructure.
The upgrade is now complete, and our services are back online.
Stronger infrastructure. Greater privacy. Same mission.
CyberKimi is launching again soon.
Stay tuned.
Adverserial AI
Every AI vendor says “we don’t train on your data.”
We made it so we can’t.
Our cyber security AI models inference runs inside hardware TEEs. Your prompts stay in the enclave. No logs. No training. E2EE. You don’t have to trust us, hardware attestation you can check yourself from Claude, Codex, Kimi, Hermes, or a web browser.
only cyber security AI models with confidential compute and verifiable zero-data-retention.
If you actually need the model not to see your customer data:
adverserial.ai/confidential/
SDK + harness plugins: github.com/AdverserialAI/con…