Secure your AI agents before they ship.

Find broken logic, security gaps, and compliance risks in your AI agents before they reach production. Analysis only — your agent never runs.

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8 findings detected

One Scanner, Every Framework

Write once, scan everywhere. Inkog's unified analysis engine works across code-first frameworks and visual builders.

How It Works

From scan to fix in seconds

Run one command. Inkog finds the risks. You ship with confidence.

1Scan
terminal
$inkog scan .
agent.py
tools.json
prompts/
LangChain AgentCustomer Support Agent
detected

Instantly recognizes your agent framework and every connected file.

2Analyze
tracing data flows...
User sends message
Agent fetches context
Input injected into prompt
LLM generates response
Send response

Traces data flow through your agent and flags where untrusted input reaches the LLM.

3Fix
inkog / findings
Critical
Prompt Injection Path

Unsanitized user input flows directly into LLM prompt template at agent.py:42

OWASP LLM01EU AI Act Art. 15
Suggested fix
42prompt = template.format(user_input=query)
42+ sanitized = sanitize_input(query)
43+ prompt = template.format(user_input=sanitized)

Pinpoints the vulnerable line and shows exactly how to fix it.

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AI Agent Security: The Missing Layer

Your stack protects code, APIs, and cloud. But who protects your agent logic?

AI agent security capability comparison across tool categories
CapabilityTraditional SASTCloud Posture (CSPM)Inkog
Hardcoded secrets
CVEs & dependencies
Open ports / IAM misconfig
Prompt injection paths
Autonomous loop detection
Tool-calling risk analysis
Human oversight gaps
EU AI Act mapping

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