Frontier security labs
for frontier AI systems.
Adversyx is an adversarial security engineering firm. We find the vulnerabilities in AI systems that dashboards miss, write the test cases for your code, and ship the patch. Human researchers, specialist AI agents, and recon engines, coordinated as one.
Adversarial security engineering is the practice of attacking AI-native software using adversary intelligence and test cases generated for that specific system, then engineering the fix.
It differs from AI red teaming as a product, which runs a fixed public corpus, and from traditional penetration testing, which does not assume the adversary writes machine-generated payloads.
An adversary lab, not a vendor.
The AI security category sold dashboards for years. Those tools tell you what to look for. They do not find anything new. We apply the discipline of cryptographic research to systems that arrived five years ago and skipped it.
Coordinated adversarial intelligence
We do not test your AI with one method. Human researchers, specialist agents, and recon engines run as a single adversarial system.
- Human researchers in the loop throughout
- Advanced specialist AI agents
- 1+ recon engine mapping the surface
Cryptographic rigor
Every finding ships with a threat model, a reproduction harness, and remediation guidance. Nothing is a black-box alert.
- JSON test cases you can re-run
- Python harnesses for CI integration
- Remediation roadmaps, not scorecards
Published research
We disclose what we find. Published methodology is the marketing. A closed dashboard is a liability.
- Coordinated disclosure with vendors
- HackerOne and Bugcrowd publication
- arXiv preprints, conference talks
Premium research-grade engagements.
Scoped per engagement, against the depth of the work rather than a package. Every engagement ends in files you own.
Attack Surface Map
Recon and adversary-informed test cases generated for one system, with reproducible artifacts for every finding.
- AI attack surface mapped
- Generated test cases, yours to run
- JSON + Python harness + threat model
Full Engagement
Generated test cases, six coordinated operators, exploitation, remediation as source, re-verified in your CI.
- Coordinated multi-agent + multi-human team
- Remediation ships as source
- Re-verified in your pipeline
AI Pentesting
Adversarial testing against deployed AI systems, combining manual exploitation with generated coverage of live attack classes.
Red Teaming
General adversary-perspective testing against infrastructure, applications, and networks using AI-assisted tradecraft.
Zero-Day Research
Discovery of novel vulnerability classes in AI systems, with full coordinated disclosure to vendors and public writeup.
AI Security Review
Comprehensive threat model and architecture risk assessment focused on prompt injection, model extraction, and agent exploitation paths.
Where the vulnerabilities actually are.
Our research agenda targets the classes that do not appear in OWASP Top 10 because OWASP has not caught up yet. Publication cadence is the moat.
LLM Agent Exploitation
Agents with tool access, memory, and delegated authority create chains that no single-scan vulnerability class covers. We map the full chain from injected instruction to privileged tool call.
Training Data Extraction
Model inversion, membership inference, and reconstruction attacks that recover training data from model outputs. The class that turns a compliance question into a breach.
Prompt Injection at System Scale
The canonical AI vulnerability class, still the least well instrumented. We develop reproducible cases that survive vendor triage and prove severity with evidence rather than assertion.
Agentic AI Governance & CyberAGI
Frontier research on the security properties of increasingly autonomous systems, and the tooling the field will need as agents gain authority.
AI agents execute. Humans decide.
The division of labor is deliberate. Agents have the throughput to cover attack-class breadth nobody could test by hand. Humans have the judgement to know which finding matters and whether the evidence holds.
We are
- Adversary-informed, built from live intel on apps like yours
- Yours-specific, test cases generated for your architecture
- Repaired, the patch is the deliverable
- Reproducible, the artifact runs in your CI
We are not
- A scanner, we do not run a corpus at your app
- A report shop, we do not hand you a PDF and leave
- Autonomous, every finding is human-reproduced
The gap nobody else fills.
No single competitor has all four: AI-native methodology, coordinated multi-method execution, research-grade publication, and cryptographic discipline.
| What you get | Pentest firms | AI-native tools | Adversyx |
|---|---|---|---|
| Publishes its findings | Yes | No | Yes |
| Writes test cases for your system | Partial | Partial | Yes |
| Runs human and agent methods together | Partial | No | Yes |
| Ships the patch, not a ticket | Partial | No | Yes |
| Runs in your CI as a regression | Yes | No | Yes |
What is AI red teaming?
AI red teaming is a coordinated adversarial engagement against an AI-native system, in which an attacker attempts to make the model leak data, bypass its controls, abuse a tool it can reach, or act outside its intended scope. Done as a product, it runs a fixed public corpus of prompts. Done as an engagement, the test cases are generated for the specific system under test.
How is this different from a prompt injection scanner?
A scanner applies a fixed payload set to a fixed target and reports what matches. It cannot find a class that was not in the corpus. We map the client's attack surface first, read what adversaries are currently running against applications of the same type, then generate test cases against that client's architecture, tools and data flows. Many of the payloads we use do not exist until we have done that work.
How is this different from an automated AI pentesting platform?
Automated platforms optimise for breadth and self-serve. We optimise for depth on one system, with human researchers in the loop who reproduce every candidate finding against the live system before it reaches you. A finding that cannot be reproduced does not get reported.
Do you fix what you find?
Yes. Remediation ships as source, sequenced by exploitability against your stack, and the same test cases are re-run in your CI to confirm the fix holds. A finding without a patch is a ticket, and a ticket is not the same as a fix.
What do we get at the end of an engagement?
A JSON test case, a Python harness, a threat model, and a remediation roadmap for every finding. The test case and harness are yours to run in CI. Nothing ships as a screenshot or a prose paragraph.
Who runs an engagement?
Human researchers, three specialist AI agents, and one recon engine, coordinated as a single system. The agents have the throughput to cover classes nobody would test by hand. The humans have the judgement to know which finding is real and whether the evidence holds.
Which frameworks do you test against?
MITRE ATLAS, the OWASP Top 10 for LLM Applications, the OWASP LLM Top 10, MITRE ATT&CK, and STRIDE for the architecture review. We map to them for reporting. We do not limit testing to them.
Do you publish what you find?
Where the vulnerability class is novel and vendor-specific detail is stripped, yes. Disclosures go through HackerOne and Bugcrowd, and the methodology is preprinted to arXiv. Client-specific findings stay with the client.
How long does an engagement take?
An Attack Surface Map is one week on one system. A Full Engagement is six to eight weeks across multiple systems. A retainer runs on a quarterly cycle.