continual-learning — agentic threat model

Not certain from the listing — The guide focuses on infrastructure rather than a specific foundation model, but the underlying LLM is highly vulnerable to indirect prompt injection that can exploit the reflection patterns and hooks described.

Focuses heavily on memory scoping. The primary threat is memory poisoning, where malicious inputs are permanently committed to the agent's long-term memory, altering its future behavior across sessions.

Directly addresses agent framework design via hooks and reflection patterns. Vulnerabilities here include insecure hook execution and flawed reflection logic that could allow an attacker to hijack the agent's execution flow.

Not certain from the listing — As an open-source skill/guide, it does not specify a hosting environment, but implementing continual learning requires secure sandboxing to prevent persistent memory from accessing host resources.

Not certain from the listing — The guide covers reflection patterns but does not explicitly detail observability or guardrails needed to detect drift or malicious memory updates over time.

Not certain from the listing — No built-in compliance, access control, or authorization policies for memory modification are mentioned in the brief description.

Not certain from the listing — While designed for coding agents, the framework could be applied to multi-agent systems where poisoned memory in one agent propagates to others through collaborative tasks.