AgentAuth — agentic threat model

Not certain from the listing — AgentAuth is compatible with popular LLMs (OpenAI, Claude, Groq) but does not host or define the foundation models itself, meaning model-level threats like adversarial reprogramming or membership inference depend entirely on the chosen external LLM provider.

Not certain from the listing — The description focuses on authentication and token management rather than RAG, vector databases, or training data operations, leaving data lineage and knowledge-base poisoning threats unaddressed.

AgentAuth directly integrates with orchestration frameworks like LangChain, Llama Index, and CrewAI. The primary threat here is insecure tool integration, where a compromised framework or malicious prompt could hijack the authenticated tool-calling capabilities provided by AgentAuth.

As an authentication solution managing OAuth tokens, API keys, and JWTs with automatic token refresh, the deployment infrastructure faces severe threats regarding secure secrets storage, token leakage in transit, and unauthorized access to the token database.

Not certain from the listing — There is no explicit mention of audit logging, token usage monitoring, or anomaly detection guardrails to identify when an agent is abusing its authenticated sessions.

This is the core layer for AgentAuth, which provides secure user credential validation and supports multiple auth methods (OAuth, JWT, API keys). Threats include authentication bypass, session hijacking, and weak token validation logic that could allow unauthorized agents to assume user identities.

Operating within multi-agent frameworks (e.g., CrewAI) and connecting to 250+ apps introduces significant ecosystem risks, such as cascading failures, agent-to-agent trust abuse where one compromised agent exploits another's authenticated session, and horizontal privilege escalation.