Autonomous Adversarial Validation

Externally security
verify anything.

A 24/7 autonomous pentester for every AI agent, API, and app you ship — powered by Pingu, our offensive intelligence engine.

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The question that matters

Not "is there a bug in the code?"
but "what can an attacker make production do — today?"

Every prompt, model, guardrail, tool, and config change reshapes your attack surface. Point-in-time pentests can't keep up.

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Chapter one

Every AI agent ships
with an unknown
attack surface.

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Point Pingu at a target

No source code.
No refusals.
No human in the loop.

Pingu discovers your product, plans attack chains, executes them end-to-end, and reports with proof — across web, API, mobile, voice, and text.

$ audn scan --target voice-agent.dogus.com [*] Discovering surface… 14 endpoints [*] Mapping tools + policies… 9 flows [*] Running 3,240 adversarial turns… [!] Prompt injection → tool bypass [!] Brand-reputation: profanity elicited [!] PII exfiltration via RAG citation ✓ Signed report — 5 crit, 9 high, 17 med
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How Pingu works

A closed adversarial loop, running on every deploy.

Red · Pingu
Attack
Adaptive attacks across prompt injection, tool misuse, PII exfil, brand-reputation, and business-logic abuse.
→
Blue · Audn
Validate
Scores every finding, maps to OWASP / NIST / EU AI Act, verifies remediation, and feeds the next attack round.
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Adversarial intelligence you can't get anywhere else
10B+
black-box attack interactions, distilled from 3,000+ pentesters and continuously refreshed outside-in intel.
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"Anything" means anything

If it responds to input, Pingu can verify it.

  • Voice AI agents
  • Text / chat agents
  • LLM APIs
  • REST & GraphQL APIs
  • Web apps
  • Mobile apps
  • Desktop clients
  • MCP & tool-use
  • RAG pipelines
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The category shift

One assists a pentester.
The other is the pentester.

Practitioner tool
Audn Autonomous
Operating model
Human-in-the-loop
Zero human-in-the-loop
Capacity
~60 hours / month
24/7 · 8,760 hours / year
Trigger
Manually initiated
Every deploy · CI/CD · schedule
Outcome
Practitioner productivity
Continuous org-wide assurance
Commercial unit
Per-seat monthly
Per concurrent agent, annual
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Authorized customer evidence · redacted

Not just findings — verified remediation.

Initial assessment
HIGH
46 consolidated findings
5 critical · 9 high · 17 med · 15 low
56 adversarial campaigns
→
After remediation + retest
MEDIUM
All 5 criticals confirmed closed
11 fully fixed · 4 partially fixed
Signed regression report
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Ships where your code ships

Plug Pingu into your release pipeline.

GitHub Actions · Azure DevOps · Jenkins. Fires on every deploy, prompt change, or guardrail update — as a release gate or continuous watch.

OWASP LLM Top 10 OWASP API Top 10 NIST AI RMF EU AI Act MITRE ATLAS ISO 42001 SOC 2 mapping
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Commercial

One price. One autonomous pentester. Always current.

Audn Enterprise
1 concurrent autonomous pentester
$75,000/ year
24/7 virtual pentesting brain · all-inclusive
  • CI/CD integration (GitHub / Azure DevOps / Jenkins)
  • Web, API, mobile, desktop, voice & text in scope
  • Latest Pingu model & attack packs, guaranteed
  • Executive + technical reports, playback, retest
  • Guided service & security-expert support
Scale up
2 concurrent agents · parallel campaigns
$150,000/ year
Same terms · twice the throughput
  • Reserved capacity across environments
  • Parallel voice + text or staging + prod
  • Turkish, English, any target language
  • Rules-of-Engagement + release-gate policy
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Today's only answer
A manual pentest. Once.
Until now.
Software development lifecycle & versions
✓
v1.0
✕
v1.1
✕
v1.2
✕
v1.3
✕
v1.4
▲
Currently in production · still not tested
▲
Manually pentested · 1 month
Released over 11 months · never tested
These require 0-human autonomous continuous pentest
Stale the moment you ship the next release — and most releases never get tested.
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What we built
An offensive AI that attacks from the outside —
and returns signed proof of every exploit.
Pinguautonomous
?black-box target
30×
cheaper
Delivered in a day by autonomous ethical-hacking robots — not weeks with a team of people.
53
TP/FN · black-box web vulns
~6× the frontier average — GPT-5.5-cyber & Claude Mythos cluster below 10.
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The moat
Why we win — and keep winning.
97%
Audn
complies to adversarial attacks
53
TP/FN
~6× the frontier average
6%
Open methods
published baseline
Two proofs. One root: the data. 10B+ interactions, distilled from 3,000+ pentesters — refreshed every day.
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The liability
You run software
you can't see the source code of.
Third-party vendor
AI agent
Voice & phone endpoint
Closed-weight LLM
Start securing them by red teaming them.
Not what's theoretically misconfigured — what's actually exploitable, right now.
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Start today

Externally security
verify anything.

Continuous proof of what attackers can actually make your software do.

Book a call →
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