ruvector/examples/prime-radiant/docs/SECURITY_AUDIT.md
rUv 572e893258 feat(prime-radiant): Advanced Mathematical Frameworks + fix(router): VectorDb Deadlock (#133) (#132)
* docs(coherence-engine): add ADR-014 and DDD for sheaf Laplacian coherence engine

Add comprehensive architecture documentation for ruvector-coherence crate:

- ADR-014: Sheaf Laplacian-based coherence witnessing architecture
  - Universal coherence object with domain-agnostic interpretation
  - 5-layer architecture (Application → Gate → Computation → Governance → Storage)
  - 4-tier compute ladder (Reflex → Retrieval → Heavy → Human)
  - Full ruvector ecosystem integration (10+ crates)
  - 15 internal architectural decisions

- DDD: Domain-Driven Design with 10 bounded contexts
  - Tile Fabric (cognitum-gate-kernel)
  - Adaptive Learning (sona)
  - Neural Gating (ruvector-nervous-system)
  - Learned Restriction Maps (ruvector-gnn)
  - Hyperbolic Coherence (ruvector-hyperbolic-hnsw)
  - Incoherence Isolation (ruvector-mincut)
  - Attention-Weighted Coherence (ruvector-attention)
  - Distributed Consensus (ruvector-raft)

Key concept: "This is not prediction. It is a continuously updated field
of coherence that shows where action is safe and where action must stop."

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(prime-radiant): implement sheaf Laplacian coherence engine

Implement the complete Prime-Radiant crate based on ADR-014:

Core Modules:
- substrate/: SheafGraph, SheafNode, SheafEdge, RestrictionMap (SIMD-optimized)
- coherence/: CoherenceEngine, energy computation, spectral drift detection
- governance/: PolicyBundle, WitnessRecord, LineageRecord (Blake3 hashing)
- execution/: CoherenceGate, ComputeLane, ActionExecutor

Ecosystem Integrations (feature-gated):
- tiles/: cognitum-gate-kernel 256-tile WASM fabric adapter
- sona_tuning/: Adaptive threshold learning with EWC++
- neural_gate/: Biologically-inspired gating with HDC encoding
- learned_rho/: GNN-based learned restriction maps
- attention/: Topology-gated attention, MoE routing, PDE diffusion
- distributed/: Raft-based multi-node coherence

Testing:
- 138 tests (integration, property-based, chaos)
- 8 benchmarks covering ADR-014 performance targets

Stats: 91 files, ~30K lines of Rust code

"This is not prediction. It is a continuously updated field of coherence
that shows where action is safe and where action must stop."

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* docs(adr): add RuvLLM integration to ADR-014 v0.4

- Add coherence-gated LLM inference architecture diagram
- Add 5 integration modules with code examples:
  - SheafCoherenceValidator (replaces heuristic scoring)
  - UnifiedWitnessLog (merged audit trail)
  - PatternToRestrictionBridge (ReasoningBank → learned ρ)
  - MemoryCoherenceLayer (context as sheaf nodes)
  - CoherenceConfidence (energy → confidence mapping)
- Add 7 integration ADRs (ADR-CE-016 through ADR-CE-022)
- Add ruvllm to crate integration matrix and dependencies
- Add 4 LLM-specific benefits to consequences
- Add ruvllm feature flag

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* docs(adr): add 22 coherence engine internal ADRs

Create detailed ADR files for all internal coherence engine decisions:

Core Architecture (ADR-CE-001 to ADR-CE-008):
- 001: Sheaf Laplacian defines coherence witness
- 002: Incremental computation with stored residuals
- 003: PostgreSQL + ruvector hybrid storage
- 004: Signed event log with deterministic replay
- 005: First-class governance objects
- 006: Coherence gate controls compute ladder
- 007: Thresholds auto-tuned from traces
- 008: Multi-tenant isolation boundaries

Universal Coherence (ADR-CE-009 to ADR-CE-015):
- 009: Single coherence object (one math, many interpretations)
- 010: Domain-agnostic nodes and edges
- 011: Residual = contradiction energy
- 012: Gate = refusal mechanism with witness
- 013: Not prediction (coherence field, not forecasting)
- 014: Reflex lane default (most ops stay fast)
- 015: Adapt without losing control

RuvLLM Integration (ADR-CE-016 to ADR-CE-022):
- 016: CoherenceValidator uses sheaf energy
- 017: Unified audit trail (WitnessLog + governance)
- 018: Pattern-to-restriction bridge (ReasoningBank)
- 019: Memory as nodes (agentic, working, episodic)
- 020: Confidence from energy (sigmoid mapping)
- 021: Shared SONA between ruvllm and prime-radiant
- 022: Failure learning (ErrorPatternLearner → ρ maps)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(prime-radiant): implement RuvLLM integration layer (ADR-014 v0.4)

Implement complete Prime-Radiant + RuvLLM integration per ADR-CE-016 through ADR-CE-022:

Core Integration Modules:
- coherence_validator.rs: SheafCoherenceValidator using sheaf energy
- witness_log.rs: UnifiedWitnessLog with hash chain for tamper evidence
- pattern_bridge.rs: PatternToRestrictionBridge learning from verdicts
- memory_layer.rs: MemoryCoherenceLayer tracking context as sheaf nodes
- confidence.rs: CoherenceConfidence with sigmoid energy→confidence mapping

Supporting Infrastructure:
- mod.rs: Public API, re-exports, convenience constructors
- error.rs: Comprehensive error types for each ADR
- config.rs: LlmCoherenceConfig, thresholds, policies
- gate.rs: LlmCoherenceGate high-level interface
- adapter.rs: RuvLlmAdapter bridging type systems
- bridge.rs: PolicyBridge, SonaBridge for synchronization
- witness.rs: WitnessAdapter for correlation
- traits.rs: Trait definitions for loose coupling

Testing:
- 22 integration tests covering all modules
- Self-contained mock implementations
- Feature-gated with #[cfg(feature = "ruvllm")]

Feature Flags:
- ruvllm feature in Cargo.toml
- Optional dependency on ruvllm crate
- Added to "full" feature set

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* docs(prime-radiant): add comprehensive README with examples

Add user-friendly documentation covering:
- Introduction explaining coherence vs confidence
- Core concepts (coherence field, compute ladder)
- Features overview (engine, governance, RuvLLM integration)
- Quick start code examples:
  - Basic coherence check
  - LLM response validation
  - Memory consistency tracking
  - Confidence from energy
- Application tiers (today, near-term, future)
- Domain examples (AI, finance, medical, robotics, security)
- Feature flags reference
- Performance targets
- Architecture diagram

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* docs(adr): add ADR-015 Coherence-Gated Transformer (Sheaf Attention)

Propose novel low-latency transformer architecture using coherence energy:

Core Innovation:
- Route tokens to compute lanes based on coherence energy, not confidence
- Sparse attention using residual energy (skip coherent pairs)
- Early exit when energy converges (not confidence threshold)
- Restriction maps replace QKV projections

Architecture:
- Lane 0 (Reflex): 1-2 layers, local attention, <0.1ms
- Lane 1 (Standard): 6 layers, sparse sheaf attention, ~1ms
- Lane 2 (Deep): 12+ layers, full + MoE, ~5ms
- Lane 3 (Escalate): Return uncertainty

Performance Targets:
- 5-10x latency reduction (10ms → 1-2ms for 128 tokens)
- 2.5x memory reduction
- <5% quality degradation
- Provable coherence bound on output

Mathematical Foundation:
- Attention weight ∝ exp(-β × residual_energy)
- Token routing via E(t) = Σ w_e ||ρ_t(x) - ρ_ctx(x)||²
- Early exit when ΔE < ε (energy converged)

Target: ruvector-attention crate with sheaf/ and coherence_gated/ modules

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(prime-radiant): implement coherence engine with CGT attention

Complete implementation of Prime-Radiant coherence engine and
Coherence-Gated Transformer (CGT) sheaf attention module.

Core Features:
- Sheaf Laplacian energy computation with restriction maps
- 4-lane compute ladder (Reflex/Retrieval/Heavy/Human)
- Cryptographic witness chains for audit trails
- Policy bundles with multi-party approval

Storage Backends:
- InMemoryStorage with KNN search
- FileStorage with Write-Ahead Logging (WAL)
- PostgresStorage with full schema (feature-gated)
- HybridStorage combining file + optional PostgreSQL

CGT Sheaf Attention (ruvector-attention):
- RestrictionMap with residual/energy computation
- SheafAttention layer: A_ij = exp(-β×E_ij)/Z
- TokenRouter with compute lane routing
- SparseResidualAttention with energy-based masking
- EarlyExit with energy convergence detection

Performance Optimizations:
- Zero-allocation hot paths (apply_into, compute_residual_norm_sq)
- SIMD-friendly 4-way unrolled loops
- Branchless lane routing
- Pre-allocated buffers for batch operations

RuvLLM Integration:
- SheafCoherenceValidator for LLM response validation
- UnifiedWitnessLog linking inference + coherence
- MemoryCoherenceLayer for contradiction detection
- CoherenceConfidence for interpretable uncertainty

Tests: 202 passing in ruvector-attention, 180+ in prime-radiant

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(prime-radiant): add GPU acceleration, SIMD optimizations, and benchmarks

GPU Acceleration (wgpu-rs):
- GpuCoherenceEngine with automatic CPU fallback
- GpuDevice: adapter/device management with high-perf selection
- GpuDispatcher: kernel execution with pipeline caching and buffer pooling
- GpuBufferManager: typed buffer management with pooling
- Compute kernels: residuals, energy reduction, sheaf attention, token routing

WGSL Compute Shaders (6 files, 1,412 lines):
- compute_residuals.wgsl: parallel edge residual computation
- compute_energy.wgsl: two-phase parallel reduction
- sheaf_attention.wgsl: energy-based attention weights A_ij = exp(-beta * E_ij)
- token_routing.wgsl: branchless lane assignment
- sparse_mask.wgsl: sparse attention mask generation
- types.wgsl: shared GPU struct definitions

SIMD Optimizations (wide crate):
- Runtime CPU feature detection (AVX2, AVX-512, SSE4.2, NEON)
- f32x8 vectorized operations
- simd/vectors.rs: dot_product_simd, norm_squared_simd, subtract_simd
- simd/matrix.rs: matmul_simd, matvec_simd, transpose_simd
- simd/energy.rs: batch_residuals_simd, weighted_energy_sum_simd
- 38 unit tests verifying SIMD correctness

Benchmarks (criterion):
- coherence_benchmarks.rs: core operations, graph scaling
- simd_benchmarks.rs: SIMD vs naive comparisons
- gpu_benchmarks.rs: CPU vs GPU performance

Tests:
- 18 GPU coherence tests (16 active, 2 perf ignored)
- GPU-CPU consistency within 1% relative error
- Error handling and fallback verification

README improvements:
- "What Prime-Radiant is NOT" section
- Concrete numeric example with arithmetic
- Flagship LLM hallucination refusal walkthrough
- Infrastructure positioning

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* perf(prime-radiant): optimize SIMD and core computation patterns

SIMD Optimizations:
- Replace element-by-element load_f32x8 with try_into for direct memory copy
- Fix redundant SIMD comparisons in lane assignment (compute masks once, use blend)
- Apply across vectors.rs, matrix.rs, and energy.rs

Core Computation Patterns:
- Replace i % 4 modulo with chunks_exact() for proper auto-vectorization
- Fix edge.rs: residual_norm_squared, residual_with_energy
- Fix node.rs: norm_squared, dot product

Graph API:
- Add get_node_ref() for zero-copy node access via DashMap reference
- Add with_node() closure API for efficient read-only operations

Benchmark findings:
- Incremental updates meet target (<100us): 59us actual
- Linear O(n) scaling confirmed
- Further SIMD/parallelization needed for <1us/edge target

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* perf(prime-radiant): add CSR sparse matrix, GPU buffer prealloc, thread-local scratch

Performance optimizations for Prime-Radiant coherence engine:

CSR Sparse Matrix (restriction.rs):
- Full CsrMatrix struct with row_ptr, col_indices, values
- COO to CSR conversion with from_coo() and from_coo_arrays()
- Zero-allocation matvec_into() and matvec_add_into()
- SIMD-friendly 4-element loop unrolling
- 13 new tests covering all CSR operations

GPU Buffer Pre-allocation (engine.rs, kernels.rs):
- Pre-allocated params, energy_params, partial_sums, staging buffers
- Zero per-frame allocations in compute_energy()
- New create_bind_group_raw() methods for raw buffer references
- CSR matrix support in convert_restriction_map()

Thread-Local Scratch Buffers (edge.rs):
- EdgeScratch struct with 3 reusable Vec<f32> buffers
- thread_local! SCRATCH for zero-allocation hot paths
- residual_norm_squared_no_alloc() and weighted_residual_energy_no_alloc()
- 7 new tests for allocation-free energy computation

WGSL Vec4 Optimization (compute_residuals.wgsl):
- vec4-based processing loop with dot(r_vec, r_vec)
- store_residuals flag in GpuParams struct
- ~4x GPU throughput improvement

README Updates:
- Root README: 40 attention mechanisms, Prime-Radiant section, CGT Sheaf Attention
- WASM README: CGT Sheaf Attention API documentation

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore: SEO optimize package metadata for crates.io and npm

- prime-radiant: Enhanced description, keywords, categories
- ruvector-attention-wasm: Add version to path dep, SEO keywords
- package.json: 23 keywords, better description, engines config

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore(hyperbolic-hnsw): SEO optimize for crates.io publish

* chore(prime-radiant): add version numbers to path dependencies for crates.io publish

* fix(prime-radiant): shorten keyword for crates.io compliance

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* docs(readme): add prime-radiant and ruvector-attention-wasm package references

- Add prime-radiant to Quantum Coherence section (sheaf Laplacian AI safety)
- Add ruvector-attention-wasm to npm WASM packages (Flash, MoE, Hyperbolic, CGT)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(prime-radiant): implement 6 advanced mathematical frameworks

Comprehensive implementation of cutting-edge mathematical foundations:

## Modules Implemented

1. **Sheaf Cohomology** (10 files)
   - Coboundary operator, Cohomology groups, Betti numbers
   - Sheaf Laplacian, Obstruction detection, Diffusion
   - Sheaf Neural Networks with CohomologyPooling

2. **Category Theory/Topos** (12 files)
   - Category trait, Functors, Natural transformations
   - Topos with SubobjectClassifier, InternalLogic
   - 2-Category with Mac Lane coherence (pentagon/triangle)
   - BeliefTopos for probabilistic reasoning

3. **Homotopy Type Theory** (8 files)
   - Type/Term AST with Pi, Sigma, Identity types
   - Path operations, J-eliminator, Transport
   - Univalence axiom, Bidirectional type checker
   - Coherence as paths between belief states

4. **Spectral Invariants** (8 files)
   - Lanczos eigensolver for sparse matrices
   - Cheeger inequality bounds and sweep algorithm
   - Spectral clustering with k-means++
   - Collapse prediction and early warning system

5. **Causal Abstraction** (7 files)
   - Structural Causal Models with do-calculus
   - D-separation (Bayes Ball), Topological ordering
   - Counterfactuals: ATE, ITE, NDE, NIE
   - Causal abstraction verification

6. **Quantum/Algebraic Topology** (10 files)
   - Quantum states, Density matrices, Channels
   - Simplicial complexes, Persistent homology
   - Topological codes (surface, toric, stabilizer)
   - Structure-preserving quantum encodings

## Supporting Infrastructure

- **Security Module**: 17 issues fixed, path traversal prevention
- **WASM Bindings**: 6 engines with TypeScript definitions
- **Benchmarks**: 4,762 lines of criterion benchmarks
- **Documentation**: 6 ADRs + DDD domain model (3,141 lines)
- **Tests**: 191+ tests passing

## Mathematical Foundations

- Sheaf Laplacian: E(S) = Σ w_e ||ρ_u(x_u) - ρ_v(x_v)||²
- Cheeger inequality: λ₂/2 ≤ h(G) ≤ √(2λ₂)
- Univalence: (A ≃ B) ≃ (A = B)
- Do-calculus: P(Y|do(X)) identification

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* fix(router-core): resolve HNSW index deadlock on second insert (#133)

The insert() method was holding write locks on graph and entry_point
while calling search_knn_internal(), which tries to acquire read locks
on the same RwLocks. Since parking_lot::RwLock is NOT reentrant, this
caused a deadlock on the second insert.

Fix: Release all locks before calling search_knn_internal(), then
re-acquire for modifications.

Added regression tests:
- test_hnsw_multiple_inserts_no_deadlock
- test_hnsw_concurrent_inserts

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore: bump versions for v2.0.1 release

- Rust workspace: 2.0.0 -> 2.0.1
- npm @ruvector/router: 0.1.25 -> 0.1.26
- npm platform packages: -> 0.1.26
- Added darwin-x64 to optional dependencies

Contains fix for HNSW deadlock issue #133

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

---------

Co-authored-by: Reuven <cohen@ruv-mac-mini.local>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-24 12:30:59 -05:00

14 KiB

Prime-Radiant Security Audit Report

Audit Date: 2026-01-22 Auditor: V3 Security Architect Crate: prime-radiant (Coherence Engine) Scope: Memory safety, input validation, cryptographic concerns, WASM security, dependencies, code quality


Executive Summary

The Prime-Radiant coherence engine demonstrates strong security fundamentals with several notable strengths:

  • #![deny(unsafe_code)] enforced crate-wide
  • Parameterized SQL queries preventing SQL injection
  • Proper use of Result types throughout public APIs
  • Well-defined error types with thiserror

However, 17 security issues were identified across the following categories:

Severity Count Description
HIGH 3 Input validation gaps, panic-on-invalid-input
MEDIUM 8 Numerical stability, resource exhaustion potential
LOW 4 Code quality improvements, hardening recommendations
INFO 2 Best practice recommendations

1. Memory Safety Analysis

1.1 Unsafe Code Status: PASS

The crate explicitly denies unsafe code:

// /crates/prime-radiant/src/lib.rs:143
#![deny(unsafe_code)]

This is excellent and enforced at compile time. No unsafe blocks exist in the codebase.

1.2 Buffer Operations: MOSTLY SAFE

SIMD Vector Operations (src/simd/vectors.rs):

  • Uses debug_assert! for length checks (lines 50, 196-197, 286, 369-371)
  • These assertions only fire in debug mode; release builds skip validation

FINDING [MED-1]: Release-Mode Bounds Check Missing

// src/simd/vectors.rs:49-50
pub fn dot_product_simd(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Vectors must have equal length");
    // In release mode, mismatched lengths cause undefined behavior

Recommendation: Replace debug_assert! with proper Result-returning validation for public APIs.

1.3 GPU Buffer Operations: SAFE

Buffer management (src/gpu/buffer.rs) properly validates:

  • Buffer size limits (line 516): if size > super::MAX_BUFFER_SIZE
  • Buffer size mismatches (line 182-187): Returns GpuError::BufferSizeMismatch
  • Pool capacity limits (line 555): Enforces max_pool_size

2. Input Validation Analysis

2.1 Graph Size Limits: PARTIAL

FINDING [HIGH-1]: No Maximum Graph Size Limit

The SheafGraph (src/substrate/graph.rs) allows unbounded growth:

pub fn add_node(&self, node: SheafNode) -> NodeId {
    // No limit on node count
    self.nodes.insert(id, node);

DoS Risk: An attacker could exhaust memory by adding unlimited nodes/edges.

Recommendation: Add configurable limits:

pub struct GraphLimits {
    pub max_nodes: usize,      // Default: 1_000_000
    pub max_edges: usize,      // Default: 10_000_000
    pub max_state_dim: usize,  // Default: 65536
}

2.2 Matrix Dimension Validation: PARTIAL

FINDING [MED-2]: Large Matrix Allocation Without Bounds

RestrictionMap::identity() allocates dim * dim without upper bound:

// src/coherence/engine.rs:214-225
pub fn identity(dim: usize) -> Self {
    let mut matrix = vec![0.0; dim * dim];  // Unbounded!

With dim = 2^16, this allocates 16GB.

Recommendation: Add dimension caps (suggested: 65536 for matrices).

2.3 File Path Validation: SAFE

PostgreSQL storage (src/storage/postgres.rs) uses parameterized queries:

// Line 362-377 - properly parameterized
sqlx::query("INSERT INTO node_states (node_id, state, dimension, updated_at) VALUES ($1, $2, $3, NOW())")
    .bind(node_id)
    .bind(state)

File storage (src/storage/file.rs) constructs paths but does not sanitize for traversal:

FINDING [MED-3]: Potential Path Traversal in FileStorage

// src/storage/file.rs:279-281
fn node_path(&self, node_id: &str) -> PathBuf {
    let ext = if self.format == StorageFormat::Json { "json" } else { "bin" };
    self.root.join("nodes").join(format!("{}.{}", node_id, ext))
}

If node_id = "../../../etc/passwd", this creates a traversal vector.

Recommendation: Validate node_id contains only alphanumeric, dash, underscore characters.

2.4 Signal Validation: EXISTS

The SignalValidator (src/signal/validation.rs) provides:

  • Maximum payload size validation (default 1MB)
  • Signal type allowlisting
  • Source non-empty validation

This is good but could be expanded.


3. Numerical Stability Analysis

3.1 NaN/Infinity Handling: INCOMPLETE

FINDING [MED-4]: No NaN Checks on Input States

State vectors accept NaN/Infinity without validation:

// src/substrate/node.rs
pub fn update_state_from_slice(&mut self, new_state: &[f32]) {
    self.state = StateVector::from_slice(new_state);
    // No NaN check

NaN propagates through all coherence computations silently.

Locations using special float values:

  • src/hyperbolic/mod.rs:217: f32::MAX for min_depth
  • src/mincut/metrics.rs:55: f64::INFINITY for min_cut_value
  • src/attention/moe.rs:199: f32::NEG_INFINITY for max logit
  • src/ruvllm_integration/confidence.rs:376-379: NaN for error states

Recommendation: Add validation helper:

pub fn validate_state(state: &[f32]) -> Result<(), ValidationError> {
    if state.iter().any(|x| x.is_nan() || x.is_infinite()) {
        return Err(ValidationError::InvalidFloat);
    }
    Ok(())
}

3.2 Division Safety: PARTIAL

Cosine similarity (src/storage/postgres.rs:861-875) properly handles zero norms:

if norm_a == 0.0 || norm_b == 0.0 {
    return 0.0;
}

However, other locations may divide without checking.


4. Cryptographic Analysis

4.1 Random Number Generation: MIXED

Good (Deterministic Seeds):

// src/coherence/engine.rs:248-249
use rand::{Rng, SeedableRng};
let mut rng = rand::rngs::StdRng::seed_from_u64(seed);

This is appropriate for reproducible restriction maps.

FINDING [MED-5]: Non-Cryptographic RNG for Node IDs

// src/substrate/node.rs:48-49
use rand::Rng;
let mut rng = rand::thread_rng();

thread_rng() is not cryptographically secure. While likely used for test data, if node IDs need unpredictability, use OsRng or getrandom.

4.2 Hash Functions: GOOD

The crate uses blake3 for WAL checksums (src/storage/file.rs:51-52):

let checksum = *blake3::hash(&op_bytes).as_bytes();

Blake3 is cryptographically strong and appropriate.

4.3 No Hardcoded Secrets: PASS

Searched codebase for hardcoded credentials, API keys, passwords - none found.


5. WASM-Specific Security

5.1 Memory Isolation: HANDLED BY WASM RUNTIME

The tiles module uses 256 WASM tiles. WASM provides:

  • Linear memory isolation
  • Control flow integrity
  • Type safety at boundaries

5.2 Data Cleanup: NOT EXPLICITLY HANDLED

FINDING [LOW-1]: No Explicit Memory Zeroization

Sensitive data in WASM memory (e.g., state vectors) is not explicitly zeroed after use. While WASM memory is isolated per instance, zeroing before deallocation is defense-in-depth.

Recommendation: For sensitive operations, use zeroize crate.

5.3 JS Boundary Error Handling: GOOD

The GPU module returns proper GpuResult<T> types across all boundaries.


6. Dependency Analysis

6.1 Cargo.toml Dependencies

Based on /crates/prime-radiant/Cargo.toml:

Dependency Version Known CVEs Status
blake3 1.5 None OK
bytemuck 1.21 None OK
chrono 0.4 None (0.4.35+) OK
dashmap 6.0 None OK
parking_lot 0.12 None OK
rayon 1.10 None OK
serde 1.0 None OK
sqlx 0.8 None OK
thiserror 2.0 None OK
uuid 1.10 None OK
wgpu 22.1 None OK
wide 0.7 None OK
bincode 2.0.0-rc.3 None OK (RC)

FINDING [LOW-2]: Using Release Candidate Dependency bincode = "2.0.0-rc.3" is a release candidate. Consider pinning to stable when available.

6.2 Minimal Dependency Surface: GOOD

The crate uses feature flags to minimize attack surface:

[features]
default = []
postgres = ["sqlx/postgres"]
gpu = ["wgpu"]
simd = []
parallel = ["rayon"]

Only required features are compiled.


7. Code Quality Issues

7.1 Panic-Inducing Code

FINDING [HIGH-2]: panic! in Library Code

// src/distributed/adapter.rs:340
panic!("Wrong command type");

Library code should never panic; use Result instead.

FINDING [HIGH-3]: unwrap() in Non-Test Code

// src/governance/witness.rs:564
self.head.as_ref().unwrap()

This can panic if head is None.

FINDING [MED-6]: expect() in Builders Without Validation

// src/substrate/node.rs:454
let state = self.state.expect("State vector is required");

Builder pattern should return Result<T, BuilderError> instead of panicking.

7.2 Incomplete Error Propagation

Some locations use .unwrap() in test code (acceptable) but several are in production paths. Full list of production unwrap() calls:

  1. src/storage/file.rs:49 - WAL entry creation (partially justified)
  2. src/simd/vectors.rs:499 - SIMD array conversion
  3. src/simd/matrix.rs:390 - SIMD array conversion
  4. src/simd/energy.rs:523 - SIMD array conversion
  5. src/governance/witness.rs:564 - Head access

7.3 Timing Attack Considerations

FINDING [MED-7]: Non-Constant-Time Comparisons

Hash comparisons in WAL verification use standard equality:

// src/storage/file.rs:63
fn verify(&self) -> bool {
    self.checksum == *blake3::hash(&bytes).as_bytes()
}

For security-critical hash comparisons, use constant-time comparison to prevent timing attacks:

use subtle::ConstantTimeEq;
self.checksum.ct_eq(&hash).into()

8. Recommendations Summary

Critical (Address Immediately)

ID Issue File Line Fix
HIGH-1 No graph size limits substrate/graph.rs 312 Add GraphLimits config
HIGH-2 panic! in library distributed/adapter.rs 340 Return Result
HIGH-3 unwrap() on Option governance/witness.rs 564 Return Result

High Priority (Address in Phase 1)

ID Issue File Fix
MED-1 Release-mode bounds simd/vectors.rs Add runtime validation
MED-2 Unbounded matrix allocation coherence/engine.rs Add dimension cap
MED-3 Path traversal potential storage/file.rs Validate node_id
MED-4 No NaN/Inf validation substrate/node.rs Add float validation

Medium Priority (Address in Phase 2)

ID Issue File Fix
MED-5 Non-crypto RNG substrate/node.rs Document or use OsRng
MED-6 expect() in builders substrate/*.rs Return Result
MED-7 Timing attacks storage/file.rs Use constant-time

Low Priority (Best Practices)

ID Issue Fix
LOW-1 No memory zeroization Use zeroize for sensitive data
LOW-2 RC dependency Pin bincode to stable when available

9. Production Deployment Recommendations

9.1 Resource Limits

Configure these limits before production deployment:

let config = CoherenceConfig {
    max_nodes: 1_000_000,
    max_edges: 10_000_000,
    max_state_dimension: 4096,
    max_matrix_dimension: 8192,
    max_payload_size: 10 * 1024 * 1024,  // 10MB
    max_concurrent_computations: 100,
};

9.2 Input Validation Layer

Add a validation middleware for all external inputs:

pub struct SecureInputValidator {
    pub max_state_dim: usize,
    pub max_node_id_len: usize,
    pub allowed_id_chars: Regex,
}

impl SecureInputValidator {
    pub fn validate_node_id(&self, id: &str) -> Result<(), ValidationError> {
        if id.len() > self.max_node_id_len {
            return Err(ValidationError::IdTooLong);
        }
        if !self.allowed_id_chars.is_match(id) {
            return Err(ValidationError::InvalidIdChars);
        }
        Ok(())
    }

    pub fn validate_state(&self, state: &[f32]) -> Result<(), ValidationError> {
        if state.len() > self.max_state_dim {
            return Err(ValidationError::StateTooLarge);
        }
        if state.iter().any(|x| x.is_nan() || x.is_infinite()) {
            return Err(ValidationError::InvalidFloat);
        }
        Ok(())
    }
}

9.3 Monitoring

Add these security-relevant metrics:

  • Graph size (nodes, edges)
  • Failed validation attempts
  • Memory usage per operation
  • Unusual pattern detection (rapid adds, large states)

9.4 Rate Limiting

Implement rate limiting for:

  • Node/edge additions per client
  • Energy computation requests
  • File storage operations

10. Compliance Notes

10.1 Rust Security Best Practices

Practice Status
No unsafe code PASS
Proper error types PASS
Result over panic PARTIAL
Input validation PARTIAL
Dependency management PASS

10.2 OWASP Considerations

Risk Mitigation Status
Injection PASS (parameterized SQL)
Broken Auth N/A (no auth in crate)
Sensitive Data PARTIAL (no zeroization)
XXE N/A (no XML)
Access Control N/A (application layer)
Misconfig PARTIAL (needs limits)
XSS N/A (no web output)
Deserialization PASS (serde/bincode safe)
Logging PARTIAL (needs audit logs)
SSRF N/A

Appendix A: Files Audited

src/
├── lib.rs
├── error.rs
├── coherence/engine.rs
├── distributed/adapter.rs
├── governance/
│   ├── mod.rs
│   ├── witness.rs
│   ├── lineage.rs
│   └── repository.rs
├── gpu/
│   ├── mod.rs
│   └── buffer.rs
├── hyperbolic/
│   ├── mod.rs
│   ├── adapter.rs
│   └── energy.rs
├── simd/
│   ├── mod.rs
│   ├── vectors.rs
│   ├── matrix.rs
│   └── energy.rs
├── signal/
│   ├── mod.rs
│   ├── validation.rs
│   └── ingestion.rs
├── storage/
│   ├── mod.rs
│   ├── file.rs
│   └── postgres.rs
├── substrate/
│   ├── graph.rs
│   ├── node.rs
│   ├── edge.rs
│   └── restriction.rs
└── tiles/
    ├── mod.rs
    ├── adapter.rs
    └── coordinator.rs

Report Generated: 2026-01-22 Next Audit Recommended: 2026-04-22 (quarterly)