Forged AI crawlers: what do they mean for secret exposure controls?

TL;DR: Automated scanners are impersonating AI crawlers from OpenAI, Anthropic, DeepSeek and other companies while requesting .env files, cloud access keys, private keys and password stores, according to GreyNoise, showing that user-agent checks alone cannot establish crawler identity or prevent secret harvesting.

Editorial analysis by NHI Mgmt Group, based on content published by GreyNoise: “Threat Actors Are Posing as OpenAI, Anthropic and DeepSeek to Target Credentials and Secrets”.

Key questions

Q: What breaks when crawler access is based only on user-agent strings?

A: User-agent-only controls fail because the header is self-declared and easy to forge.

Q: Why do forged crawlers create a secret-exposure risk even without a breach?

A: They create risk because the scanner's objective is to discover files that already contain usable secrets.

Q: What are the signs that crawler impersonation is failing your controls?

A: Repeated requests for /robots.txt are a positive crawler signal, while direct requests for credential files are not.

Practitioner guidance

• Verify crawler identity against published address ranges Require a match between the claimed crawler name and the operator's published source IP ranges before granting any access exception or control waiver.
• Remove secrets from web-accessible paths Audit web roots, build artefacts and application directories for .env files, credentials directories and backup files, then relocate or remove anything that contains live secrets.
• Alert on credential file requests Create detections for requests to /.env, /.aws/credentials, /.git/config and similar high-risk paths because legitimate crawlers have no reason to request them.

Bottom line: Forged AI crawler traffic turns ordinary web requests into a credential-discovery channel when secret files remain publicly reachable.

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