ML/AI Security Frameworks

• ML/AI Security Frameworks are structured, layered approaches that map and mitigate threats unique to autonomous systems, including adversarial inputs and memory poisoning.
• They employ defense-in-depth strategies with real-time anomaly detection, risk quantification, and rigorous cross-layer controls to ensure robust system security.
• These frameworks integrate modular threat modeling and empirical validations, enhancing resilience by localizing risks across all operational layers.

Machine Learning and AI Security Frameworks comprise structured, often layered approaches for mitigating, detecting, and managing threats unique to ML/AI systems. These frameworks address not only conventional security risks such as data breaches and denial-of-service, but also threats emerging from autonomy, adversarial robustness, memory manipulation, supply-chain integrity, and the dynamic interactions of agentic (LLM-driven or tool-using) systems. The following sections provide a comprehensive review of core principles, methodologies, practical applications, and ongoing research challenges defining state-of-the-art ML/AI security frameworks.

1. Layered and Modular Threat Modeling Architectures

Modern AI security frameworks leverage modular, multi-layered architectures to provide precise threat mapping and facilitate defense-in-depth. The MAESTRO framework (Zambare et al., 12 Aug 2025) exemplifies this for agentic AI, structuring threat modeling across seven layers:

MAESTRO Layer	Security Scope
L1	Foundation Models (LLMs, inference engines)
L2	Data Operations (aggregation, logging, memory)
L3	Agent Frameworks (planning, memory logic, orchestration)
L4	Deployment & Infrastructure (APIs, microservices, containers)
L5	Evaluation & Observability (telemetry, anomaly detection)
L6	Security & Compliance (auth, audit, regulatory, forensics)
L7	Agent Ecosystem (inter-agent/user interaction, multi-agent)

This layered architecture enables mapping threats to localized domains (e.g., memory poisoning at L2, planner misuse at L3), supports cross-layer propagation analysis, and allows for targeted and overlapping controls in defense.

2. Expanding the Threat Taxonomy: From Classic Risks to AML-Specific Vectors

ML/AI security extends classic threat taxonomies by incorporating adversarial machine learning (AML) and autonomy-driven risks. Core threat classes include:

• Resource Exhaustion DoS: Induced by high-volume traffic or replay attacks, demonstrably causing latency and degraded anomaly detection (e.g., telemetry update interval increases from 7–8s to 13s+) (Zambare et al., 12 Aug 2025).
• Memory/Knowledge Base Poisoning: Low-level log or parameter tampering (e.g., inserting synthetic high-severity events into agent memory) triggers cascading resource exhaustion, logic drift, and system-wide detection delays (Zambare et al., 12 Aug 2025).
• Agentic/Planner Exploitation: Compromising adaptation logic or cross-component communication channels leads to unintended or hazardous system actions.
• Adversarial Input & Tool Abuse: Attackers leverage input perturbations or "tool poisoning" (malicious tool descriptions or behaviors) to subvert inference or system control (Narajala et al., 11 Apr 2025).

Risk assessment frameworks like FRAME (Shapira et al., 24 Aug 2025) quantify these threats across feasibility, impact, and empirical likelihood, reflecting deployment environment, attacker capabilities, and attack success rates drawn from curated AML case records.

3. Defense-in-Depth and Resilience Strategies

Security frameworks emphasize multilayered, defense-in-depth approaches, mapping controls directly to architectural layers. Key defensive mechanisms include:

Layer	Core Controls
L1 (Models)	Guardrails, output filtering, aligned fine-tuning
L2 (Data Ops)	Memory/log isolation, validation, access restriction
L3 (Agent)	Planner validation, rule-based constraints, chain verification
L4 (Infra)	Containerization, least-privilege, API/network hardening
L5 (Obs.)	Telemetry, drift monitoring, statistical/ML anomaly detection
L6 (Security)	Auth, audit trails, forensic/causal traceability
L7 (Ecosystem)	Multi-agent trust models, access control

Mechanisms include real-time anomaly detection, forensic logging, automated rollback, input/output validation, content sanitization, and layered access restriction. No single control suffices; resilience is achieved via overlapping, fail-operational barriers at every layer (Zambare et al., 12 Aug 2025, Narajala et al., 11 Apr 2025).

4. Quantitative Risk Analysis and Prioritization

Standardized ordinal scoring supports operational risk analysis. The MAESTRO framework (Zambare et al., 12 Aug 2025) utilizes:

$R = P \times I \times E$

where $P$=likelihood, $I$=impact, $E$=exploitability (each 1–3 scale).

Practical risk mapping reveals, for example, resource DoS as high likelihood/impact/exploitability ($R=27$), memory poisoning as medium likelihood/high impact/medium exploitability ($R=12$). FRAME (Shapira et al., 24 Aug 2025) extends this quantification by integrating context-specific feasibility, measurable impact, and empirical success rate from an attack dataset, producing actionable, ranked risk outputs.

5. Insights from Real-world Implementation and Case Studies

Empirical validation demonstrates the practical viability of multi-layered frameworks. Key findings include:

• Memory integrity is pivotal: Simple log/file tampering drives systemic misbehavior; maintaining and validating memory boundaries prevents escalation of otherwise localized faults (Zambare et al., 12 Aug 2025).
• Adaptation logic and cross-layer communication are critical attack surfaces: Adversarial manipulation can rapidly propagate from memory or data flows to agent planning, output actions, and resource consumption, demanding monitoring and strong authentication across internal interfaces.
• Operational threat localization and response: Layered threat mapping allows for rapid identification and containment. For example, alerting on cross-layer anomalies can trigger rollback or forced memory isolation before full compromise.
• Resilience via ongoing evaluation and formal verification: Sustained system reliability is reinforced by ongoing threat discovery, risk re-prioritization, and architectural/formal methods validation.

A plausible implication is that frameworks lacking memory validation and internal adaptation monitoring remain acutely susceptible to both targeted and systemic failures, regardless of robust external controls.

6. Comparison with Traditional and Contemporary Security Frameworks

MAESTRO's layer-based operational threat modeling offers greater precision in agentic-AI systems compared to monolithic models like STRIDE or PASTA, which were not designed to encompass the dynamic and memory-centric features of autonomous AI (Zambare et al., 12 Aug 2025, Narajala et al., 11 Apr 2025). MAESTRO's viability is demonstrated in real agent implementations using Python, LangChain, FastAPI, WebSockets, Docker, and standard packet-capture stacks, providing evidence of its applicability in high-threat, operational environments and supporting both risk scoring and resilient system design.

7. Conclusion and Outlook

ML/AI security frameworks have evolved towards explicit, modular, and data-driven threat modeling, risk quantification, and defense-in-depth principles. The synthesis of layered architectures, attack/defense taxonomies, and empirical validation has advanced both engineering resilience and operational robustness. Future developments will likely focus on integrating formal verification into the adaptation/planning logic, automating cross-layer threat propagation tracking, and scaling real-time auditability as agentic AI adoption expands.