AI Agent Detection

Detect rogue agents and AI-speed attackers

AI agents are gaining access to your infrastructure, and attackers are moving at machine speed. Tracebit deploys canary resources that catch both the moment they touch something they shouldn't.

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The problem

AI is a new attack surface, and a new attacker

AI agent usage is growing rapidly, and Tracebit is already detecting rogue agents in production environments. Attackers are increasingly using GenAI to rapidly develop exploits and autonomously orchestrate full attack chains. Machine-speed attacks don't wait for manual triage.

+89%

YoY increase in AI-enabled adversary operations

CrowdStrike 2026 Global Threat Report

30+

organizations targeted by a state-sponsored group running autonomous AI attacks

Anthropic disclosure, November 2025

52%

of employees willing to use AI even if it violates policy

CalypsoAI Insider AI Threat Report 2025

What the research says

Build deception within 90 days

CSA's post-Mythos guidance, shaped by 250+ CISOs, names deception a priority action.

See Tracebit's Gemini CLI research for a concrete example of an AI attack path exposed through deception.

CSA: The Mythos-ready Security Program

Deploy deception liberally

The CSA's response to the first fully autonomous agentic attack on Hugging Face.

See Tracebit's analysis of the OpenAI / Hugging Face breach for how deception maps to each stage of the disclosed attack.

CSA: Hugging Face Incident Post-Mortem

Detection coverage

Detection across AI agent threat scenarios

From prompt injection to agent compromise, canaries detect when AI goes wrong.
Stage
Attack
Detection
Shadow AI
Employees run unsanctioned agents with no security oversight
Any unsanctioned agent touching a canary announces itself
Agent Credential Theft
Agent credentials stolen from repos, endpoints, or pipelines
Alert at first use of canary credentials, against cloud or decoy services
Prompt Injection · OWASP LLM01
Attacker manipulates agent via malicious input
Hijacked agent touches canary resources outside its scope
Data Exfiltration · OWASP LLM02
Compromised agent extracts sensitive data
Read operations on canary storage
Supply Chain Compromise · OWASP LLM03
Compromised dependency or build accesses secrets
Canary credentials in repos and CI, and decoy registries, alert on use
Excessive Agency · OWASP LLM06
Agent ignores instructions and operates beyond its boundaries
Read and list operations on canary resources

How it works

Detect actions an AI agent should never perform

AI agents can be instructed, tricked, or compromised into taking actions outside their intended task. Tracebit places realistic canaries across your environment and alerts when an agent opens, uses, or modifies one. Interaction with a canary is a high-confidence signal that an agent has crossed a deception boundary and needs investigation.

Step 1

Connect platforms where AI operates

Integrate your cloud accounts, Kubernetes clusters, endpoints and repositories

Step 2

Deploy canaries for prohibited actions

Tracebit places realistic decoy files, credentials, endpoints and resources so actions that should never be part of an agent’s task trigger an alert.

Step 3

Alert on prohibited agent actions

Opening, using or modifying a canary triggers an immediate alert. Certain canaries can stop malicious agents mid-run.

Step 4

Investigate with context

See exactly which agent, credential, or IP accessed the canary resource.

Protect your environment with Tracebit

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