Adala — agentic threat model

Integrates with external LLMs for core tasks like classification and summarization, exposing the system to prompt injection, adversarial reprogramming, and mis-aligned outputs that could corrupt labeling pipelines.

Processes ground truth datasets and performs data labeling. This creates a high risk of data poisoning if malicious inputs are introduced into the training or labeling sets, potentially biasing the agent's learned behavior.

Utilizes an extensible agent framework with dynamic memory. Vulnerabilities in memory storage/retrieval or modular extensions could lead to memory poisoning or unauthorized tool execution.

Not certain from the listing — as an open-source framework, deployment and infrastructure security (including sandboxing, secrets management, and container isolation) are entirely dependent on the user's hosting environment.

Relies on human feedback and ground truth datasets for evaluation and learning. While this provides a feedback loop, it is vulnerable to evaluation gaming or drift if the feedback channel is compromised.

Not certain from the listing — there is no mention of built-in enterprise security controls, role-based access control (RBAC), or compliance certifications in the public repository description.

Not certain from the listing — although the framework supports 'agents' in plural, the listing does not detail multi-agent communication protocols, trust boundaries, or marketplace risks.