AI security audit platform for production systems
Uncover the real risks across all your AI systems
From invisible risk to actionable findings
Run adversarial testing, prioritize risk, and get technical evidence with concrete mitigation steps.
The hidden attack surface
Systems that read data, make decisions, and take action inside your organization can be manipulated. Jailbreaks, data leakage, and prompt injection turn them into another entry point in your attack chain.
Predictive models
Classifiers and automated decision systems, tested against manipulation and evasion.
Generative AI
LLMs and assistants exposed to jailbreaks, data leakage, and prompt injection.
Agentic systems
Agents that act and execute tasks, tested against permission abuse and attack chains.
Built on recognized standards
Adversalia findings are classified and documented following the industry's reference frameworks for AI security.
OWASP LLM Top 10
The international reference standard for vulnerabilities in LLM-powered applications. Adversalia covers all 10 categories with exploitation evidence.
MITRE ATLAS
Adversarial threat framework for machine learning systems, developed by MITRE Corporation. The foundation of Adversalia's threat model.
MITRE ATT&CK
Knowledge base of real-world adversary tactics and techniques, applied to generative AI systems and autonomous agents.
NIST AI RMF
AI risk management framework from the National Institute of Standards and Technology. Reference for severity classification and remediation guidance.
How it works
System reconnaissance
Adversalia analyzes the target system, its capabilities, integrations, and active controls to map the real attack surface.
Agent deployment
Multiple specialized agents work in parallel: some probe system behavior, others dig deeper into findings and refine attack vectors.
Adaptive attack
Agents adjust strategy based on the system's responses, uncovering vulnerabilities that static testing techniques can't catch.
Findings report
Vulnerabilities ranked by severity, mapped to the OWASP LLM Top 10, with exploitation evidence and concrete remediation guidance.
Early access
Apply to try Adversalia
Get invite-only access to an initial assessment of one AI system. Spots are available until this stage's capacity is filled.
The assessment includes one adversarial test, a summary of results, and a sample of the evidence collected.
Apply for early accessApplying doesn't guarantee access. Invitations are assigned based on system fit and availability for this stage.
Annual plans
Essential
Annual commitment of USD 4,188 billed in twelve monthly installments.
One-time annual payment with a 10% discount off the total of twelve installments.
Professional
Annual commitment of USD 10,284 billed in twelve monthly installments.
One-time annual payment with a 10% discount off the total of twelve installments.
Institutional
Annual commitment of USD 27,120 billed in twelve monthly installments.
One-time annual payment with a 10% discount off the total of twelve installments.
Enterprise
Annual commitment with scope and payment terms defined per organization.
Annual plans billed monthly. Access during the initial stage will be granted by invitation, based on available capacity.
Frequently asked questions
Direct answers about AI security audits, AI red teaming, and LLM pentesting.
What is an AI security audit?
An AI security audit is a technical assessment that uncovers exploitable vulnerabilities in AI systems: predictive models, LLMs, and autonomous agents. It combines targeted adversarial testing with an analysis of the system's integrations and active controls. The output is a report of findings ranked by severity, backed by technical evidence and concrete mitigation guidance.
How is this different from traditional pentesting?
Traditional pentesting evaluates infrastructure, networks, and applications against known vulnerabilities and standard exploitation techniques. Adversarial testing for AI systems instead targets model behavior: jailbreaks, data leakage, prompt injection, and permission abuse in autonomous agents. It requires methodologies and tooling built specifically for systems that reason and act non-deterministically.
What is AI red teaming?
AI red teaming is a continuous adversarial process where specialized agents simulate real attackers against an AI system. Unlike a one-off test, it adjusts strategy based on the system's responses, mirroring how a determined adversary would probe for weaknesses. Adversalia runs AI red teaming with multiple agents working the same target in parallel, which also strengthens AI agent security specifically.
Which frameworks are applied during an audit?
Adversalia classifies and documents findings against recognized standards: the OWASP Top 10 for LLM Applications, the MITRE ATLAS adversarial threat framework, and the NIST AI RMF for risk management. These frameworks guide both AI security audits and LLM pentesting, and help align remediation with compliance work, including the adversarial testing requirements introduced by the EU AI Act.
What do you get at the end of an audit?
You get a report with findings ranked by severity, reproducible exploitation evidence, and concrete mitigation guidance for each vulnerability. The report is mapped to the OWASP LLM Top 10 and ready to support certification or internal audit processes. It also includes an executive summary written for non-technical stakeholders.
How long does an engagement take?
Duration depends on scope and the number of systems being evaluated. During the early access stage, each engagement covers an initial assessment of one AI system: reconnaissance, adaptive attack deployment, and delivery of the final report. Exact timelines are agreed during the application process, based on the system's complexity.
What does Adversalia's post-quantum readiness mean?
Adversalia builds its platform with post-quantum readiness for AI in mind, so the security of its assessments doesn't rely on cryptographic primitives that quantum computing could eventually weaken. This matters for organizations already planning for medium-term compliance, including the new adversarial testing requirements under the EU AI Act. It doesn't replace a dedicated cryptography audit — it's part of the platform's long-term security posture.
Ready to audit your AI system?
Get ahead of the AI security gap before it catches up with you. Schedule an audit with our team.
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