www-project-ai-testing-guide/Document/content/4.2_Appendix_B.md

4.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification

AI models operate at massive scale, rely on highly dynamic data flows, and often function as opaque “black boxes,” which can introduce unexpected failure modes and attack surfaces. To address this, the DIE Triad, Distributed, Immutable, Ephemeral offers a complementary, resilience-focused framework. Originally proposed by Sounil Yu [20], the DIE model shifts the focus from traditional data protection toward system survivability, adaptability, and architectural robustness. In AI contexts, where model drift, adversarial inputs, and dependency chains are constant concerns, applying the DIE principles helps ensure that AI systems are not only secure, but also resilient by design, capable of withstanding disruption and degradation.

While CIA (Confidentiality, Integrity, Availability) focuses on protecting data and systems from unauthorized access and disruption, DIE shifts the focus to reducing the value and longevity of attack targets through design principles that emphasize:

1. Distributed – Systems should not rely on single points of failure. Workloads, data, and services are spread across nodes to ensure resilience, scalability, and fault tolerance.

2. Immutable – Components, especially code and data, should not be modified after deployment. This limits tampering, simplifies rollback, and ensures auditability and predictability.

3. Ephemeral – Systems should be short-lived by design. Containers, functions, sessions, and data should expire quickly, reducing attack windows and minimizing exposure in case of compromise.

Where CIA is about protecting data, DIE is about designing systems that are inherently resistant to compromise, even if breached. Together, CIA and DIE provide a complementary security and resilience strategy, particularly important in AI, serverless, and microservices environments. Where CIA is excellent for defining security objectives like preventing unauthorized access or ensuring uptime, DIE focuses on architectural resilience and trustworthiness, especially important for: Cloud-native AI systems, LLM agents and plugin architectures and Federated learning and edge AI.

Many modern AI system failures (e.g., data remnants in shared GPUs, hot-reloaded LLM agents, or drift in distributed pipelines, are violations of DIE principles, not just CIA.)

Below is a structured explanation showing how DIE (Distributed, Immutable, Ephemeral) threats differ from and complement the CIA (Confidentiality, Integrity, Availability) model specifically in the context of AI systems aligned to the SAIF (Secure AI Framework) architecture.

DIE vs CIA in SAIF-Aligned AI Architectures
SAIF Layer	DIE Property	Example DIE Threat	Why It’s Not Fully Covered by CIA
Infrastructure	Ephemeral	Data residue in serverless inference functions	CIA assumes static data protection, but doesn’t assess whether environments are truly short-lived.
Infrastructure	Immutable	Improper model immutability in CI/CD pipelines	CIA covers unauthorized changes but not immutability guarantees for reproducibility and rollback.
Data	Distributed	Loss of data provenance in federated training	CIA protects data in motion/rest but not consistency or lineage in distributed environments.
Model	Immutable	Self-modifying inference logic	CIA may flag unauthorized updates but not dynamic, internal logic changes (e.g., prompt tuning).
Application	Ephemeral	Persistent caching in chatbots or RAG systems	CIA addresses data exposure but not requirements for automatic expiration or zero-trace design.
Infrastructure	Distributed	Inconsistent access policies across multi-cloud deployments	CIA checks policy presence but not synchronization or enforcement across distributed systems.

Here is the completed DIE (Distributed, Immutable, Ephemeral) threat mapping for SAIF components #1–#18.

Application Layer - DIE Threats Mapping
SAIF Component	DIE Threats (Distributed, Immutable, Ephemeral)
#1 - User	Distributed: User impersonation across multiple access points;Immutable: Insecure session storage allowing manipulation;Ephemeral: Persistent session tokens or non-expiring authentication.
#2 - User Input & Output	Distributed: Interception at multiple points of UI/API;Immutable: Unvalidated input leading to state corruption;Ephemeral: Cached responses leading to stale or replayed outputs.
#3 - Application	Distributed: Lateral movement within app clusters;Immutable: Tampered configs or injected runtime logic;Ephemeral: Long-lived processes vulnerable to memory attacks.
#4 - Agents/Plugins	Distributed: Chained plugin abuse;Immutable: Modified plugin payloads;Ephemeral: Persistent plugin state leaking data.
#5 - External Sources	Distributed: Manipulated feeds at source or in transit;Immutable: Lack of integrity validation on ingested data;Ephemeral: Reliance on long-lived static external datasets.

Model Layer - DIE Threats Mapping
SAIF Component	DIE Threats (Distributed, Immutable, Ephemeral)
#6 - Input Handling	Distributed: Input abuse across endpoints;Immutable: Bypass of sanitization layers;Ephemeral: Delayed reprocessing of malicious data.
#7 - Output Handling	Distributed: Output leaking via multiple channels;Immutable: Spoofed or altered model responses; Ephemeral: Retention of unsafe inference results.
#8 - Model Usage	Distributed: Repeated or coordinated inference abuse;Immutable: Malicious prompts altering inference pathways; Ephemeral: Caching of results exposing stale outputs.

Infrastructure Layer - DIE Threats Mapping
SAIF Component	DIE Threats (Distributed, Immutable, Ephemeral)
#9 - Model Storage Infrastructure	Distributed: Replicated stolen models; Immutable: Hash mismatch undetected; Ephemeral: Residual temp artifacts after use.
#10 - Model Serving Infrastructure	Distributed: Scalable attack surface across nodes; Immutable: Corrupted container images; Ephemeral: Persistent sockets vulnerable to abuse.
#11 - Model Evaluation	Distributed: Result leakage across evaluations; Immutable: Fake metrics stored long-term; Ephemeral: Testing artifacts reused in prod.
#12 - Model Training & Tuning	Distributed: Federated poisoning attacks; Immutable: Compromised checkpoints; Ephemeral: Retained outdated training data.
#13 - Model Frameworks & Code	Distributed: Infected libraries used across builds; Immutable: Code injection in frameworks; Ephemeral: Exploitable debug files not purged.
#14 - Data Storage Infrastructure	Distributed: Data exfiltration via synced systems;Immutable: Stale corrupted backups;Ephemeral: Temporary stores left exposed.

Data Layer - DIE Threats Mapping
SAIF Component	DIE Threats (Distributed, Immutable, Ephemeral)
#15 - Training Data	Distributed: Poisoning across datasets; Immutable: Bad data fixed in only one copy; Ephemeral: Non-rotated sensitive data.
#16 - Data Filtering & Processing	Distributed: Filter evasion via edge node abuse; Immutable: Faulty transformations undetected; Ephemeral: Processed data not purged timely.
#17 - Internal Data Sources	Distributed: Compromise through internal systems; Immutable: Corrupt reference records; Ephemeral: Excessive query logs or retained queries.
#18 - External Data Sources	Distributed: Public API abuse; Immutable: No integrity checks on ingestion; Ephemeral: Source content reused unsafely.