Attack Chain

1. Attacker identifies an exposed application interface that interacts with flyto-core and allows user-supplied parameters.
2. Attacker crafts a payload specifically targeting the base_url parameter within the library's llm.chat or ai.model functions.
3. Attacker points base_url to a custom-controlled public web server capable of capturing inbound HTTP headers.
4. The victim application receives the malicious request and passes the base_url parameter to the vulnerable library functions.
5. flyto-core performs an SSRF check, which validates that the attacker's public URL is not a private IP, allowing the request to proceed.
6. The library retrieves the operator's secret key from environment variables (e.g., OPENAI_API_KEY).
7. The library transmits a POST request to the attacker's server, attaching the secret key in the Authorization: Bearer header.
8. Attacker logs the incoming request, extracts the Bearer token, and uses it to perform unauthorized API calls on behalf of the victim.

Impact

Successful exploitation results in the exfiltration of high-privilege cloud LLM and vector database credentials. Attackers can leverage these keys to incur fraudulent billing costs, exfiltrate sensitive data accessible to the LLM or vector store, or manipulate internal AI workflows. The ease of triggering this via the library's exposed API surfaces makes this a critical risk for any organization utilizing vulnerable versions of flyto-core.

Recommendation

1. Upgrade flyto-core to version 2.26.7 or higher immediately to apply the vendor-supplied patches (CVE-2026-67425).
2. Audit application code to identify all calls to llm.chat, ai.model, llm.agent, and vector.connector where user-controlled input influences the base_url parameter.
3. Implement strict allowlisting for allowed API endpoints rather than relying on SSRF guards for credential-handling components.
4. Rotate any API keys that were potentially exposed in environment variables if the application was reachable by untrusted actors.