Internal and external repositories of structured data, unstructured documents, and graph-based representations that provide authoritative information for grounding.
AI/ML / Multi Agent Refarch / Capabilities / DEV
Authoritative knowledge source bases
CCC.MARefArc.CP12
Related Threats
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.TH12 | Indirect prompt injection via retrieved or processed content | Malicious instructions hidden in retrieved documents, web-search results, tool outputs, or persisted memory are processed by an agent and hijack its decision-making, escalate privileges, trigger unauthorized actions, or exfiltrate data, which is especially dangerous in automated multi-agent workflows. |
| CCC.MARefArc.TH18 | RAG grounding failures | Even with retrieval, responses may contradict retrieved documents, drop caveats truncated by the context window, fill gaps with incorrect general knowledge, exceed authorized advisory scope, or adopt an inappropriate tone or certainty for the domain. |
| CCC.MARefArc.TH22 | Poor-quality, drifting, and bias-amplifying data | Inaccurate, incomplete, outdated, or biased grounding and training data lead to unreliable outputs, while data and concept drift erodes predictive power over time and amplifies historical errors at scale. |
| CCC.MARefArc.TH23 | Discriminatory outputs from bias | Biased training data, architectural and feature choices, proxy variables such as postal codes, and uncorrected feedback loops cause systematically discriminatory outcomes against protected groups, with legal and reputational exposure. |
| CCC.MARefArc.TH32 | Credential harvesting via agent tools and storage | Agents are manipulated into using file, database, API, and cloud-management tools to enumerate and extract credentials from configuration files, environment variables, process memory, databases, key vaults, and instance metadata, and to correlate fragments into full credentials. |