Key Components
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Language Models (LLMs)
The core cognitive engine or 'brain' of agentic systems, responsible for understanding, reasoning, planning, and generating responses. (NIST: Data, Model; OWASP: KC1; AISVS: C8)
Security: LLMs can hallucinate, be manipulated, or leak sensitive data. Ensure prompt validation, output filtering, and model alignment.
Implementation: Apply prompt hardening, output validation, and continuous monitoring. Reference: OWASP KC1, AISVS C8.
🗄️
Memory Modules
Enable agents to retain information across interactions, with varying scope and security boundaries. (NIST: Data; OWASP: KC4; AISVS: C8)
Security: Memory can be poisoned, manipulated, or used for privilege escalation. Validate and sanitize all memory operations.
Implementation: Apply memory validation, access controls, and regular audits. Reference: OWASP KC4, AISVS C8.
🎛️
Orchestration (Control Flow)
Mechanisms that dictate agent behavior, information flow, and decision-making. (NIST: Governance, Map; OWASP: KC2; AISVS: C9)
Security: Orchestration can be attacked via intent breaking, identity spoofing, or communication poisoning. Use secure protocols and role validation.
Implementation: Implement secure communication, role validation, and monitoring. Reference: OWASP KC2, AISVS C9.
🛠️
Tool Integration Frameworks
Allow agents to extend capabilities by using external tools, APIs, and functions. (NIST: Manage; OWASP: KC5; AISVS: C6)
Security: Tool integration can introduce risks of misuse, privilege compromise, and code execution. Use sandboxing and privilege controls.
Implementation: Apply tool sandboxing, privilege controls, and monitoring. Reference: OWASP KC5, AISVS C6.
🌐
Operational Environment (Agencies)
Capabilities that allow agents to interact with external systems and environments, posing varying levels of risk. (NIST: Manage; OWASP: KC6; AISVS: C5)
Security: Operational environments can be abused for privilege escalation, code injection, or data exfiltration. Apply least privilege and runtime controls.
Implementation: Enforce least privilege, runtime controls, and continuous monitoring. Reference: OWASP KC6, AISVS C5.
🧩
Reasoning / Planning Paradigm
Paradigms that enable AI agents to solve complex problems by breaking down tasks, making decisions, and forming plans. (NIST: Map, Measure; OWASP: KC3; AISVS: C7)
Security: Reasoning paradigms can be manipulated for misaligned behaviors or goal manipulation. Validate reasoning chains and outputs.
Implementation: Apply multi-stage validation, fact-checking, and output filtering. Reference: OWASP KC3, AISVS C7.
🔎
Retrieval-Augmented Generation (RAG)
Integrates external knowledge sources and retrieval mechanisms with generative models to enhance reasoning and output accuracy. (NIST: Data, Knowledge; OWASP: RAG; AISVS: C10)
Security: RAG systems are vulnerable to data poisoning, retrieval manipulation, and knowledge source compromise. Validate retrieved data and monitor connector integrity.
Implementation: Apply retrieval validation, source whitelisting, and output filtering. Monitor for anomalous retrieval patterns. Reference: OWASP RAG, AISVS C10.
🗂️ Framework References