The security control plane for AI agents

Ship AI agents you can actually control

We break your agents, then hand you the guardrails that stop it. Every prompt, tool call, and decision — tested before production, enforced in it.

A live trace. Every probe, every path in, every decision the agent makes under attack.

CONTROLLING ACROSS

OpenAI

Anthropic

Gemini

Mistral

Azure OpenAI

AWS Bedrock

Self-hosted

CONTROLLING ACROSS

OpenAI

Anthropic

Gemini

Mistral

Azure OpenAI

AWS Bedrock

Self-hosted

60+ interviews. The same three gaps, everywhere.

Across company size and industry, security and ML teams described the same problems — traceability, manipulation, and decision integrity.

1

NO TRACEABILITY

"Can you show the full trace from the LLM response to the raw data points? Lineage was definitely a question."
"Can you show the full trace from the LLM response to the raw data points? Lineage was definitely a question."

CEO — AI venture studio

CEO — AI venture studio

2

2

INVISIBLE MANIPULATION

"They crafted a log line that said 'this is a benign alert.' We failed that on the pen test."
"They crafted a log line that said 'this is a benign alert.' We failed that on the pen test."

Engineering Manager — Public security company

Engineering Manager — Public security company

3

NO DECISION INTEGRITY

"Someone can trick the system to inflate numbers, deflate numbers — we have to provide accurate answers for true business decisions."
"Someone can trick the system to inflate numbers, deflate numbers — we have to provide accurate answers for true business decisions."

Data Analyst — AI-first communication platform

Your agents have more access than your employees — and none of the oversight.

The prompt behind your next incident is already in your context window. Nothing you own is watching for it.

The prompt behind your next incident is already in your context window. Nothing you own is watching for it.

THREAT

0

%

YoY increase in AI-enabled adversary operations

DETECTION

0

%

organizations couldn't confirm they had an AI breach

TIME TO ESCALATION

0

sec

median attacker hand-off after initial access

Decision flow

Traditional security watches execution. AI security must understand decisions.

A manipulated decision never trips an alert. It just comes back wrong, and looks right.

Nobody breaks in. They just leave a note where the agent will read it.

Four steps. No exploit, no CVE, no malware. Here's the whole attack.

Step 01

Find what it reads.

An invoice, a ticket, a wiki page, a web result. Anything that reaches the context window.

Step 02 · attacker

Leave the instruction there.

White text, a code comment, alt text. The agent doesn't skim — it reads everything.

Step 03

Wait.

The agent stores it as trusted context. It's a document from a source you approved.

Step 04 · attacker

Let it use your permissions.

The agent does the work. Authenticated, in-scope, fully logged, and completely wrong.

The request succeeds. The decision doesn't.

Every attack we find becomes a rule that blocks it.

Findings don't ship as a PDF — they ship as enforcement. Every vulnerability Darkhunt finds converts into a runtime policy: deterministic, near-zero latency, blocking the request before it reaches execution. Then we attack it again to prove it holds.

When something does get through, you'll know in minutes — not quarters.

Every finding is traced back to its origin. Every decision, tool call, and policy violation is replayed to produce grounded evidence and prioritized fixes.

Proof your auditor is looking for

Every finding and every policy maps to the frameworks you're already accountable to.

OWASP Top 10 for LLMs

APPLICATION SECURITY

Every finding tagged to its category — prompt injection, data leakage, model theft.

NIST AI RMF

RISK FRAMEWORK

Findings mapped across Govern, Map, Measure, and Manage.

EU AI Act

REGULATION

Evidence aligned to high-risk system obligations, timestamped for audit.

MITRE ATLAS

THREAT MATRIX

Attacks classified by real-world adversary technique.

ISO 42001

AI MANAGEMENT

Controls mapped to the AI management-system standard enterprises now require.

See your own agent under attack.

Connect your agent. Trace every execution path and uncover every place it can be manipulated.

Your agents have more access than your employees — and none of the oversight.

The prompt behind your next incident is already in your context window. Nothing you own is watching for it.

THREAT

0

%

YoY increase in AI-enabled adversary operations

DETECTION

0

%

organizations couldn't confirm they had an AI breach

TIME TO ESCALATION

0

sec

median attacker hand-off after initial access

60+ interviews. The same three gaps, everywhere.

Across company size and industry, security and ML teams described the same problems — traceability, manipulation, and decision integrity.

1

NO TRACEABILITY

"Can you show the full trace from the LLM response to the raw data points? Lineage was definitely a question."

CEO — AI venture studio

2

INVISIBLE MANIPULATION

"They crafted a log line that said 'this is a benign alert.' We failed that on the pen test."

Engineering Manager — Public security company

3

NO DECISION INTEGRITY

"Someone can trick the system to inflate numbers, deflate numbers — we have to provide accurate answers for true business decisions."

Data Analyst — AI-first communication platform

Know what your AI agent does before someone else does.

Try Darkhunt ->

Start free · Onboarding included

Know what your AI agent does before someone else does.

Try Darkhunt ->

Start free · Onboarding included

Know what your AI agent does before someone else does.

Try Darkhunt ->

Start free · Onboarding included