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ANN-DXENC RFC Draft v1.0.0 — Part V

ANN-DXENC RFC Draft v1.0.0 — Part V

PRIVATE AND PROPRIETARY. Owned by Kanjani AI Research & Causum. See NOTICE.md.

Security Considerations

This section defines:

  • adversarial threat models
  • poisoning vectors
  • sovereignty threats
  • computational attacks
  • linguistic/semantic attacks
  • model-level attacks
  • supply-chain threats
  • integrity and lineage protections
  • cross-plane exploitation attempts

This section is normative. All compliant systems MUST enforce the requirements described herein.

25. Security Philosophy

Digital DNA is designed to address the fundamental problem:

AI systems cannot protect themselves from corrupted data, corrupted cognition, or corrupted memory without an identity layer that spans all cognitive planes.

Without Digital DNA, an attacker can:

  • inject poison into training
  • corrupt memory consolidation
  • manipulate context windows
  • invert intent in reasoning
  • cross-contaminate tenants
  • induce vector drift
  • exploit semantic ambiguity
  • bypass metadata-based lineage controls

Digital DNA provides:

  • genetic identity
  • multi-layer immunity
  • sovereign isolation
  • lineage enforcement
  • drift detection
  • firebreaks
  • phenotype/blocking controls

This makes poisoning attacks detectable, containable, and often impossible.

26. Threat Model Overview

Attackers are classified along three dimensions:

26.1 Intent

  • Malicious (P3/P4)
  • Opportunistic
  • Unintentional (P1/P2)
  • Stochastic/Adversarial AI Agents

26.2 Capability

  • Low capability (script-kiddie poisoning)
  • Moderate capability (semantic manipulation)
  • High capability (embedding-space manipulation)
  • Sovereign-capable (state-backed, multi-vector)

26.3 Scope

  • Single CSR
  • Memory cluster
  • Cross-tenant vector poisoning
  • Systemic Cognitive Fabric compromise

Digital DNA defenses scale with threat level.

27. Attack Surface Categories

The system MUST assume the following attack surfaces are exploitable:

27.1 Data-Plane Attacks (Extraction & Birth)

A. Synthetic Content Injection

Attacker injects falsified or contradictory source data.

Defense:

  • glyph validation
  • dialect enforcement
  • birth CRC
  • manifold base-shape validation
  • genotype immunology

B. Schema Poisoning

Attacker manipulates input structure.

Defense:

  • strict canonicalization
  • schema-bound genotype constraints

C. Sovereignty Spoofing

Attacker presents data as belonging to a different tenant.

Defense:

  • sovereign_key validation
  • dialect mismatch rejection

27.2 Reasoning-Plane Attacks (Cognition & Inference)

A. Phenotypic Drift Manipulation

Attacker attempts to force cognition into unstable or adversarial states.

Defense:

  • phenotype ↔ genotype coherence checks
  • manifold drift detection
  • firebreak activation

B. Semantic Subversion

Attacker manipulates meaning through ambiguous phrasing or adversarial tokens.

Defense:

  • semantic-sense coherence scoring
  • procedural-sense alignment checks

C. Intent Inversion Attack

Attacker targets the procedural sense to change intent (e.g., request → command).

Defense:

  • procedural-glyph + manifold correlation
  • intent boundary enforcement

27.3 Outcome-Plane Attacks (Memory Corruption)

A. Memory Insertion Attack

Attacker attempts to store poisoned CSR in long-term memory.

Defense:

  • phenotype anomaly detection
  • manifold stability threshold
  • genotype integrity checks
  • vaccine class filtering

B. Knowledge Drift Attack

Attacker increases exposure to borderline conflicting CSRs.

Defense:

  • vaccine class V4 consolidation controls
  • memory-lineage enforcement

C. Sovereign Memory Pollution

Attacker injects cross-tenant contaminated records.

Defense:

  • strict sovereign antibodies (V3/V4)
  • dialect mismatch
  • keyspace mismatch

28. Sovereignty Threats

Because the Cognitive Fabric supports multiple sovereign Digital-Entities, cross-tenant poisoning is a critical security concern.

28.1 Sovereign Boundary Attacks

Attackers attempt:

  • to mimic another tenant’s DNA
  • to bypass dialect segregation
  • to inject records into foreign cognitive domains
  • to exploit shared model layers for leakage

Defense Mechanisms:

  1. Tenant-specific sovereign keys (K_dna, K_m, K_v).
  2. Dialect-specific glyph subsets.
  3. Per-tenant vaccine definitions.
  4. Manifold isolation (per-tenant topological space).
  5. Cross-tenant disallowance (default).

A Digital-Entity’s DNA MUST NOT be compatible with any other entity.

28.2 Colluding Tenant Attack

Two compromised tenants attempt cross-poisoning.

Defense:

  • Sovereign mismatch detection
  • Vaccine-class isolation
  • Firebreak across tenant-boundaries
  • Manifold topology segregation
  • Cross-tenant CSR rejection enforced by RFC MUST-level rules

29. Model-Level Attacks

These attacks target the LLM or vector models supporting the Cognitive Substrate.

29.1 Embedding Drift Injection

Attacker modifies embeddings to shift manifold geometry.

Digital DNA Defense:

  • manifold drift threshold checks
  • CRC validation
  • recalculation by sovereign manifold parameters

29.2 Token-Level Adversarial Attacks

Attacker uses adversarial phrasing to manipulate CSR phenotype.

Digital DNA Defense:

  • sense-glyph anchored decoding
  • semantic ↔ procedural sense coherence detection

29.3 RAG Poisoning

Attacker pollutes source documents.

Digital DNA Defense:

  • DNA enforced on ingestion
  • vaccine-class enforcement
  • phenotype consistency tests

30. Cognitive Fabric–Level Threats

When reasoning and memory form a global intelligence layer, systemic risks appear.

30.1 Cognitive Collapse Attack

Attacker injects:

  • contradictory clusters
  • high-volume synthetic noise
  • subtly drifting phenotypes

Goal: collapse reasoning consensus and destabilize memory.

Defenses:

  • topology-aware manifold guardians
  • multi-plane vaccine enforcement
  • memory consolidation filters
  • cluster stability scoring
  • firebreak (VX-FIREBREAK)

30.2 Chain-of-Thought Subversion

Attacker injects malicious CoT reasoning.

Defense:

  • CoT-consistency genotype alignment
  • reasoning-plane phenotype gating
  • vaccine class V4 mandatory for chain-of-thought storage

30.3 Temporal Poisoning

Manipulating situational context (timeline deception).

Defense:

  • situational glyph consistency
  • temporal manifold validation

31. Security Properties Guaranteed by Digital DNA

A compliant implementation MUST provide the following guarantees:

31.1 Integrity

  • Records cannot be altered without detection.
  • Phenotype cannot override genotype.
  • Memory cannot be corrupted without triggering firebreak.

31.2 Provenance

  • Every CSR has a verifiable birth identity.
  • Lineage survives all plane transitions.

31.3 Sovereign Isolation

  • No cross-tenant transfusion of cognition.
  • No universal poisoning method exists.

31.4 Immunological Defense

  • Poisoning attempts trigger immune responses automatically.
  • Embedding drift is always detectable above threshold.

31.5 Controlled Mutation

  • Reasoning changes cannot alter Digital DNA.
  • Mutation classes impose hard limits on cognitive evolution.

31.6 Memory Sanctity

  • Only validated CSRs become permanent knowledge.
  • Memory cannot be coerced or influenced by adversarial phenotype.

32. Residual Risks and Limitations

Even with Digital DNA:

32.1 Super-Sovereign Adversaries

Nation-state or AGI attackers may attempt to:

  • mimic dialects
  • reproduce manifolds
  • break sovereign key hierarchies

Mitigations exist but cannot guarantee absolute isolation.

32.2 Human-Layer Vulnerabilities

Operator error (e.g., mislabeling sources) may create false positives/negatives.

32.3 Non-Deterministic Models

Stochastic LLM behavior can complicate phenotype validation.

32.4 Multi-Modal Attacks

Advanced adversaries may combine:

  • visual
  • auditory
  • textual
  • temporal
  • social engineering

Digital DNA reduces but cannot eliminate all such risks.