KC4 · Memory Modules
In-agent cross-user memory (KC4.5)
Memory limited to a single agent but shared across multiple users.
- 6 threats
- 18 controls
- 0 architectures
Threats
T1 Memory Poisoning
Attackers inject malicious data into the agent's memory to manipulate future decisions, affecting any memory type from in-agent session to cross-agent cross-user memory.
T3 Privilege Compromise
Breaking information system boundaries through context collapse, causing unauthorized data access/leakage, or exploiting tool privileges to gain unauthorized access to systems.
T5 Cascading Hallucination Attacks
Foundation models generate incorrect information that propagates through the system, affecting reasoning quality and being stored in memory across sessions or agents.
T6 Intent Breaking & Goal Manipulation
Attacks that manipulate the agent's core decision-making to achieve unauthorized goals, breaking control flow, abusing shared context, or interfering with isolated data.
T8 Repudiation & Untraceability
Making agent actions difficult to trace through workflow manipulation, obscuring decision trails in reasoning chains, or tampering with evidence in memory.
T12 Agent Communication Poisoning
Injection of malicious data into inter-agent communication channels or using external systems for side channel communications and memory persistence.
Controls
- M1 Memory Validation & Sanitization: Implement comprehensive validation and sanitization of AI memory systems, including vector databases, embeddings, and training data to prevent poisoning attacks and ensure data integrity throughout the AI lifecycle.
- M2 Tool Sandboxing & Isolation: Implement defense-in-depth isolation for tool execution environments using containerization, virtualization, and strict resource controls to prevent tool misuse, contain breaches, and prevent unexpected code execution.
- M3 Secure Inter-Agent Communication: Implement cryptographically secure communication channels between agents with authentication, integrity verification, replay protection, and support for decentralized identity to prevent spoofing, poisoning attacks, and rogue agent infiltration.
- M4 Prompt Hardening & Jailbreak Prevention: Implement multiple layers of prompt security including structural defenses, behavioral constraints, runtime detection, and advanced techniques to prevent prompt injection, model manipulation, and jailbreaking attempts.
- M5 Multi-Stage Reasoning Validation: Implement comprehensive validation of agent reasoning processes using multiple checkpoints, external validators, consistency checks, and fact verification to ensure goal alignment, prevent manipulation, and detect hallucinations.
- M6 Comprehensive Security Monitoring & Auditing: Implement end-to-end observability with security-focused monitoring, anomaly detection, tamper-evident logging, and advanced threat hunting to ensure accountability, enable incident response, and detect rogue agents.
- M7 Zero Trust & Least Privilege Access: Implement fine-grained access controls with continuous verification, temporary credentials, minimal permission sets, and dynamic privilege management following zero trust principles to prevent tool misuse, privilege compromise, and unauthorized code execution.
- M8 Adaptive Human-in-the-Loop Controls: Implement intelligent human oversight with risk-based approval workflows, clear decision interfaces, anti-fatigue mechanisms, and adaptive trust systems to prevent approval overwhelm, human attacks, and manipulation while maintaining effective oversight.
- M10 Defense-in-Depth Architecture & Trust Boundaries: Implement multiple security layers with clear trust boundaries, network segmentation, cryptographic verification, and zero-trust networking to contain breaches, prevent lateral movement, and protect against privilege compromise, identity spoofing, and rogue agents.
- M11 Content Security & Output Filtering: Implement comprehensive filtering and validation of agent outputs to prevent harmful content generation, data leakage, manipulation attempts, and ensure compliance with security policies and regulatory requirements.
- M12 Secure Model Training & Validation: Comprehensive security practices for model training pipelines including data integrity verification, training environment hardening, model validation against adversarial inputs, and ensuring reproducibility of training processes.
- M13 Secure Development Lifecycle Integration: Embed security practices throughout the AI and agent lifecycle with security design reviews, automated testing gates, dependency scanning, and continuous compliance checks.
- M14 Runtime Security Controls & Sandboxing: Implement comprehensive runtime security controls including advanced sandboxing, execution monitoring, and dynamic security policy enforcement to prevent unexpected code execution and contain security breaches in real-time.
- M15 Agent Identity & Authentication Framework: Implement comprehensive agent identity management with decentralized identifiers, verifiable credentials, and strong authentication to prevent identity spoofing, enable secure agent discovery, and support agent reputation systems.
- M16 Behavioral Monitoring & Ethics Framework: Implement comprehensive behavioral analysis, ethics enforcement, and bias detection to identify misaligned behaviors, prevent human manipulation, and ensure ethical agent operations while maintaining transparency and accountability.
- M17 Supply Chain Security & Provenance: Implement comprehensive supply chain security including model provenance verification, dependency management, secure development environments, and third-party component validation to prevent supply chain attacks and ensure component integrity.
- M18 Incident Response & Recovery Framework: Implement comprehensive incident response capabilities specifically designed for agentic AI systems, including automated containment, forensic analysis, recovery procedures, and lessons learned integration to rapidly respond to security incidents and minimize impact.
- M19 MCP Server Hardening & Tool Provenance: Treat every MCP server, A2A peer and tool definition as an untrusted, versioned dependency: admit only allow-listed and verified servers, authenticate and authorize every protocol request, validate and screen tool metadata and results before they reach the model, and require re-approval when a tool definition changes.
Source: OWASP Securing Agentic Applications Guide (KC1-KC6 sub-components) 1.0 (Jul 2025) · retrieved 2026-11