ruvnet--RuView/rust-port/wifi-densepose-rs/crates/ruv-neural/SECURITY_REVIEW.md

ruv-neural Crate System: Security and Performance Review

Date: 2026-03-09 Version: 0.1.0 Scope: All 12 workspace crates in the ruv-neural system Status: Implementation checklist for v0.1 and v0.2 milestones

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Table of Contents

1. Crate Inventory
2. Security Review
   • Input Validation
   • Memory Safety
   • Data Privacy
   • Network Security (ESP32)
   • Supply Chain
   • Findings from Code Audit
3. Performance Review
   • Computational Complexity
   • Memory Usage
   • Optimization Opportunities
   • ESP32 Constraints
   • Benchmarking Recommendations
   • Performance Findings from Code Audit
4. Action Items

---

Crate Inventory

Crate	Status	Lines (approx)	Role
ruv-neural-core	Implemented	~500	Types, traits, error types, RVF format
ruv-neural-sensor	Implemented	~170	Sensor data acquisition, calibration, quality
ruv-neural-signal	Implemented	~450	Filtering, spectral analysis, Hilbert, connectivity
ruv-neural-graph	Stub	~2	Graph construction from signals
ruv-neural-mincut	Implemented	~700	Stoer-Wagner, spectral cut, Cheeger, dynamic tracking
ruv-neural-embed	Implemented	~350	Spectral, topology, node2vec embeddings
ruv-neural-memory	Implemented	~425	Embedding store, HNSW index
ruv-neural-decoder	Implemented (lib)	~25	KNN, threshold, transition decoders
ruv-neural-esp32	Implemented	~265	ADC interface, sensor readout
ruv-neural-wasm	Stub	~2	WebAssembly bindings
ruv-neural-viz	Implemented (lib)	~20	Visualization, ASCII rendering, export
ruv-neural-cli	Stub	~2	CLI binary

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Security Review

Input Validation

All public APIs must validate their inputs at system boundaries. This section catalogs each validation requirement and its current status.

Sensor Data Validation

Check	Required In	Status	Notes
sample_rate_hz > 0	MultiChannelTimeSeries::new	MISSING	Constructor accepts sample_rate_hz without validating it is positive and finite. Division by zero in duration_s() if zero.
num_channels > 0	MultiChannelTimeSeries::new	PASS	Returns error if data.len() == 0.
Channel lengths equal	MultiChannelTimeSeries::new	PASS	Validates all channels have the same length.
Non-NaN/Inf values	All signal processing	MISSING	No validation that input signals contain only finite f64 values. NaN propagation through FFT, PLV, and connectivity metrics produces silent garbage.
num_samples > 0	AdcReader::read_samples	PASS	Returns error if num_samples == 0.
Channel count > 0	AdcReader::read_samples	PASS	Returns error if no channels configured.
Channel index bounds	AdcReader::load_buffer	PASS	Returns ChannelOutOfRange error.
sensitivity > 0	SensorChannel	MISSING	sensitivity_ft_sqrt_hz is a public field with no validation on construction.
sample_rate > 0	SensorChannel	MISSING	sample_rate_hz is a public field with no validation.

Recommendation: Add a SensorChannel::new() constructor that validates sensitivity_ft_sqrt_hz > 0, sample_rate_hz > 0, and that the orientation vector is a unit normal. Add sample_rate_hz > 0 and sample_rate_hz.is_finite() checks to MultiChannelTimeSeries::new. Add a validate_finite() utility for signal data.

Graph Construction Validation

Check	Required In	Status	Notes
Edge indices < num_nodes	BrainGraph::adjacency_matrix	PARTIAL	Silently skips out-of-bounds edges rather than reporting an error. This masks data corruption.
Edge weight is finite	BrainGraph	MISSING	BrainEdge.weight is not validated. NaN/Inf weights propagate silently through Stoer-Wagner and spectral analysis.
num_nodes >= 2	stoer_wagner_mincut	PASS	Returns proper error.
num_nodes >= 2	fiedler_decomposition	PASS	Returns proper error.
num_nodes >= 2	SpectralEmbedder::embed	PASS	Returns proper error.
num_nodes >= 2	cheeger_constant	PASS	Returns proper error.
Self-loops	BrainGraph	MISSING	No validation that source != target on edges. Self-loops could inflate degree calculations.

Recommendation: Add a BrainGraph::validate() method that checks all edge indices are within bounds, weights are finite, and no self-loops exist. Call it from stoer_wagner_mincut, spectral_bisection, and SpectralEmbedder::embed. Consider making adjacency_matrix() return Result with an error for out-of-bounds edges instead of silently ignoring them.

RVF Format Validation

Check	Required In	Status	Notes
Magic bytes	RvfHeader::validate	PASS	Validates against RVF_MAGIC.
Version	RvfHeader::validate	PASS	Rejects unknown versions.
Header length	RvfHeader::from_bytes	PASS	Checks bytes.len() < 22.
Data type tag	RvfDataType::from_tag	PASS	Returns error for unknown tags.
metadata_json_len overflow	RvfFile::read_from	CONCERN	metadata_json_len is cast from u32 to usize and used to allocate a Vec. A malicious file with metadata_json_len = u32::MAX (~4 GB) would cause an OOM allocation.
Payload length	RvfFile::read_from	CONCERN	read_to_end reads unbounded data into memory. A malicious file could exhaust memory.
JSON validity	RvfFile::read_from	PASS	Uses serde_json::from_slice which returns an error on invalid JSON.
num_entries vs actual data	RvfFile::read_from	MISSING	The header declares num_entries and embedding_dim, but these are never cross-checked against the actual payload size.

Recommendation: Add maximum size limits for metadata_json_len (e.g., 16 MB) and total payload size. Validate that num_entries * entry_size_for_type <= data.len() after reading. Use Read::take() to cap reads.

Embedding Validation

Check	Required In	Status	Notes
Non-empty vector	NeuralEmbedding::new (core)	PASS	Returns error for empty vectors.
Non-empty vector	NeuralEmbedding::new (embed)	PASS	Returns error for empty vectors.
Dimension match	cosine_similarity, euclidean_distance	PASS	Returns DimensionMismatch error.
Zero-norm handling	cosine_similarity	PASS	Returns 0.0 for zero-norm vectors.
NaN/Inf in vector	NeuralEmbedding::new	MISSING	No check for non-finite values in the embedding vector.

Memory Store Validation

Check	Required In	Status	Notes
Capacity > 0	NeuralMemoryStore::new	MISSING	Capacity 0 is accepted, producing a store that evicts on every insertion.
k > 0	query_nearest	MISSING	k=0 produces an empty result silently (acceptable but undocumented).
Dimension consistency	NeuralMemoryStore::store	MISSING	No check that all stored embeddings have the same dimensionality. Mixed dimensions cause silent errors in query_nearest.

JSON Parsing

Check	Status	Notes
Uses serde derive	PASS	All types use #[derive(Serialize, Deserialize)]. No manual parsing anywhere.
No unsafe JSON parsing	PASS	Standard serde_json throughout.

---

Memory Safety

Check	Status	Notes
No unsafe code	PASS	Zero unsafe blocks across all crates.
Vec instead of raw pointers	PASS	All data structures use Vec, HashMap, BinaryHeap.
ndarray for matrix ops	NOT USED	Despite being listed in workspace.dependencies, matrix operations use Vec<Vec<f64>> throughout. This is bounds-checked but less efficient.
No C FFI	PASS	No FFI calls. ESP32 code uses pure Rust types.
No std::mem::transmute	PASS	None found.
No std::ptr usage	PASS	None found.
Bounds checking on slices	PASS	Uses .get(), iterator methods, and Rust's built-in bounds checks.
Integer overflow	CONCERN	max_raw_value() in adc.rs casts (1u32 << resolution_bits) - 1 to i16. If resolution_bits > 15, this overflows silently. Currently only 12 or 16 are intended, but 16 produces i16::MAX wrapping.

Recommendation: Add a validation check on resolution_bits in AdcConfig (must be <= 15 for i16 representation, or switch to u16/i32). Consider migrating Vec<Vec<f64>> matrix representations to ndarray::Array2<f64> for better cache performance and built-in bounds checking.

---

Data Privacy

Neural data is among the most sensitive personal data categories. This section covers data handling practices.

Check	Status	Notes
No PII in log messages	NEEDS AUDIT	The crate uses tracing in workspace dependencies but currently has no tracing::info! or tracing::debug! calls with data fields. As logging is added, ensure neural data values, subject IDs, and session IDs are never logged at INFO level or below.
No neural data in error messages	PASS	Error messages contain structural information (dimensions, indices, version numbers) but not raw signal values or embeddings.
subject_id handling	CONCERN	EmbeddingMetadata.subject_id is stored as plaintext Option<String>. This is PII that is included in serialized embeddings (serde), HNSW indices, and RVF files.
session_id handling	CONCERN	Same concern as subject_id.
Memory store encryption	NOT IMPLEMENTED	NeuralMemoryStore holds embeddings in plaintext Vec<f64>. No encryption-at-rest.
Memory zeroization on drop	NOT IMPLEMENTED	Embedding data is not zeroed when dropped. Sensitive neural data persists in deallocated memory.
WASM data boundary	STUB	WASM crate is not yet implemented. When implemented, must ensure no neural data is sent to external services without explicit user consent.
RVF file privacy	CONCERN	RvfFile serializes metadata as JSON, which may contain subject_id. No option to strip or anonymize metadata before export.

Recommendations:

• Implement a Redactable trait for types that may contain PII, providing redact() and anonymize() methods.
• Use the zeroize crate to zero sensitive data on drop for NeuralEmbedding, NeuralMemoryStore, and MultiChannelTimeSeries.
• Add a strip_pii() method to RvfFile that removes or hashes identifiers before export.
• Document privacy responsibilities in each crate's module documentation.
• For v0.2: Add optional encryption-at-rest for NeuralMemoryStore using ring or aes-gcm.

---

Network Security (ESP32)

Check	Status	Notes
Node ID authentication	NOT IMPLEMENTED	ESP32 crate (ruv-neural-esp32) is currently a local ADC reader with no network protocol. When TDM protocol is added, node IDs must be authenticated.
CRC32 integrity	NOT IMPLEMENTED	No data packet framing or integrity checks exist yet.
TLS encryption	NOT IMPLEMENTED	v0.1 has no network layer. Planned for v0.2.
Packet size limits	NOT IMPLEMENTED	No packet protocol exists yet.
Buffer overflow prevention	PARTIAL	AdcReader uses a fixed-size ring buffer (4096 samples), which prevents unbounded growth. However, load_buffer silently truncates data that exceeds buffer size rather than reporting it.
DMA configuration	N/A	dma_enabled is a configuration flag only; actual DMA is not implemented in std mode.

Recommendations for v0.2 TDM Protocol:

• Authenticate node IDs using a pre-shared key or challenge-response.
• Add CRC32 or CRC32-C to every data packet.
• Set maximum packet size to 1460 bytes (single WiFi frame MTU).
• Use DTLS or TLS 1.3 for encryption when available.
• Rate-limit incoming packets per node to prevent flooding.
• Validate all fields in received packets before processing.

---

Supply Chain

Check	Status	Notes
Minimal dependencies	PASS	Core dependencies: thiserror, serde, serde_json, num-complex, rustfft, rand. All are well-maintained, widely-used crates.
No proc macros except serde	PASS	Only serde's derive macros and thiserror's derive macro are used. clap's derive is CLI-only.
All deps from crates.io	PASS	No git dependencies or path dependencies outside the workspace.
Workspace-managed versions	PASS	All dependency versions are declared in [workspace.dependencies].
petgraph usage	UNUSED	Listed in workspace dependencies but not imported by any crate. Remove to reduce supply chain surface.
tokio usage	UNUSED	Listed in workspace dependencies but not imported by any crate. Remove unless async is planned.
ruvector-* crates	UNUSED	Five RuVector crates listed but not imported by any workspace member. Remove unused dependencies.
Cargo.lock	PRESENT	Cargo.lock is committed, ensuring reproducible builds.

Recommendation: Run cargo deny check to audit for known vulnerabilities. Remove unused workspace dependencies (petgraph, tokio, ruvector-* crates) to minimize attack surface. Add cargo audit to CI.

---

Findings from Code Audit

SEC-001: RVF Unbounded Allocation (Severity: Medium)

Location: ruv-neural-core/src/rvf.rs, line 193

let mut meta_bytes = vec![0u8; header.metadata_json_len as usize];

A crafted RVF file with metadata_json_len = 0xFFFFFFFF allocates 4 GB. Similarly, read_to_end on line 201 reads unbounded data.

Fix: Add maximum size constants and validate before allocating:

const MAX_METADATA_LEN: u32 = 16 * 1024 * 1024; // 16 MB
const MAX_PAYLOAD_LEN: usize = 256 * 1024 * 1024; // 256 MB

if header.metadata_json_len > MAX_METADATA_LEN {
    return Err(RuvNeuralError::Serialization(
        format!("metadata_json_len {} exceeds maximum {}", header.metadata_json_len, MAX_METADATA_LEN)
    ));
}

SEC-002: Missing Sample Rate Validation (Severity: Medium)

Location: ruv-neural-core/src/signal.rs, MultiChannelTimeSeries::new

The sample_rate_hz parameter is not validated. A value of 0.0 causes division by zero in duration_s(). A negative or NaN value causes incorrect spectral analysis throughout the pipeline.

Fix: Add validation in the constructor:

if sample_rate_hz <= 0.0 || !sample_rate_hz.is_finite() {
    return Err(RuvNeuralError::Signal(
        format!("sample_rate_hz must be positive and finite, got {}", sample_rate_hz)
    ));
}

SEC-003: NaN Propagation in Signal Processing (Severity: Low)

Location: ruv-neural-signal/src/connectivity.rs, all functions

If either input signal contains NaN, the Hilbert transform produces NaN outputs, which propagate silently through PLV, coherence, and all connectivity metrics. The result is a brain graph with NaN edge weights, which causes undefined behavior in Stoer-Wagner (infinite loops or wrong results).

Fix: Add a validate_signal helper and call it at entry points:

fn validate_signal(signal: &[f64]) -> Result<()> {
    if signal.iter().any(|x| !x.is_finite()) {
        return Err(RuvNeuralError::Signal("Signal contains NaN or Inf values".into()));
    }
    Ok(())
}

SEC-004: Integer Overflow in ADC (Severity: Low)

Location: ruv-neural-esp32/src/adc.rs, AdcConfig::max_raw_value

pub fn max_raw_value(&self) -> i16 {
    ((1u32 << self.resolution_bits) - 1) as i16
}

For resolution_bits = 16, this computes 65535 as i16 = -1, which causes incorrect voltage conversion (division by -1 flips sign).

Fix: Change return type to u16 or i32, or validate resolution_bits <= 15.

SEC-005: HNSW Visited Array Allocation (Severity: Low)

Location: ruv-neural-memory/src/hnsw.rs, search_layer, line 261

let mut visited = vec![false; self.embeddings.len()];

This allocates a visited array proportional to the total number of embeddings on every search call. For large indices (100K+ embeddings), this causes unnecessary allocation pressure. More critically, if entry is >= self.embeddings.len(), the indexing on line 262 panics.

Fix: Use a HashSet<usize> instead of a boolean array for sparse visitation. Add bounds check on entry.

---

Performance Review

Computational Complexity

Operation	Complexity	Target Latency	Current Status
FFT (1024 points)	O(N log N)	<1 ms	Implemented via rustfft (SIMD-optimized). Meets target.
Hilbert transform	O(N log N)	<1 ms	Two FFTs (forward + inverse). Meets target for N <= 4096.
PLV (channel pair)	O(N) + 2x FFT	<0.5 ms	Calls hilbert_transform twice. Meets target for N <= 2048.
Coherence (channel pair)	O(N) + 2x FFT	<0.5 ms	Same as PLV.
Connectivity matrix (68 regions)	O(N^2 x M)	<10 ms	M = samples per channel, N = 68: 2,278 Hilbert pairs. May exceed target for long windows.
Stoer-Wagner mincut (68 nodes)	O(V^3)	<5 ms	68^3 = ~314K operations. Meets target.
Spectral embedding (68 nodes)	O(V^2 x k x iterations)	<3 ms	With k=8, iterations=100: 68^2 x 8 x 100 = ~37M ops. May be tight.
Fiedler decomposition	O(V^2 x iterations)	<2 ms	1000 iterations x 68^2 = ~4.6M ops. Meets target.
Cheeger constant (exact, n<=16)	O(2^n x n^2)	<5 ms	Exponential but capped at n=16: 65K x 256 = ~16M ops. Meets target.
HNSW insert	O(log N x ef x M)	<1 ms	ef=200, M=16: ~3200 distance computations per insert. Meets target.
HNSW search (10K embeddings)	O(log N x ef)	<1 ms	ef=50: ~50-200 distance computations. Meets target.
Brute-force NN (10K embeddings)	O(N x d)	<5 ms	d=256, N=10K: 2.56M f64 ops. Acceptable but HNSW preferred.
Full pipeline (68 regions)	-	<50 ms	Sum of above stages. Should meet target.

Memory Usage

Component	Calculation	Size
64-channel x 1000 Hz x 8 bytes x 1s	64 x 1000 x 8	512 KB per second
Brain graph adjacency (68 nodes)	68^2 x 8 bytes	~37 KB
Brain graph adjacency (400 nodes)	400^2 x 8 bytes	~1.25 MB
Single embedding (256-d)	256 x 8 bytes	2 KB
Memory store (10K embeddings, 256-d)	10K x 2 KB	~20 MB
HNSW index (10K, M=16, 256-d)	10K x (2KB + 16 x 16 bytes)	~22.5 MB
Stoer-Wagner working memory (68 nodes)	2 x 68^2 x 8 + 68 x vec overhead	~75 KB
Spectral embedder (68 nodes, k=8)	k x 68 x 8 + Laplacian 68^2 x 8	~41 KB
RVF file in memory	header + metadata + payload	Variable, unbounded (see SEC-001)

Optimization Opportunities

Immediate (v0.1)

1. Eliminate redundant Hilbert transforms in connectivity matrix

   • compute_all_pairs calls hilbert_transform twice per channel pair.
   • For 68 channels, this means 68 x 67 = 4,556 Hilbert transforms instead of 68.
   • Fix: Pre-compute analytic signals for all channels, then compute metrics pairwise.
   • Expected speedup: ~67x for connectivity matrix computation.

2. Replace Vec<Vec> with flat Vec for adjacency matrices

   • Current Vec<Vec<f64>> has poor cache locality due to heap-allocated inner Vecs.
   • Fix: Use Vec<f64> with manual row-major indexing, or migrate to ndarray::Array2<f64>.
   • Expected speedup: 2-4x for matrix-heavy operations (Stoer-Wagner, Laplacian).

3. Avoid Vec::remove(0) in eviction

   • NeuralMemoryStore::evict_oldest calls self.embeddings.remove(0), which is O(n).
   • Fix: Use a VecDeque or circular buffer.
   • Expected speedup: O(1) eviction instead of O(n).

4. Pre-allocate FFT planner

   • compute_psd, compute_stft, and hilbert_transform each create a new FftPlanner per call.
   • Fix: Cache the planner or use a thread-local planner.
   • Expected speedup: Eliminates repeated plan computation.

Medium-term (v0.2)

5. Rayon for parallel channel processing

   • compute_all_pairs iterates channel pairs sequentially.
   • Fix: Use rayon::par_iter for the outer loop.
   • Expected speedup: Linear with core count for connectivity computation.

6. SIMD for distance computations in HNSW

   • Euclidean distance in HnswIndex::distance uses scalar iteration.
   • Fix: Use packed_simd2 or auto-vectorization hints.
   • Expected speedup: 4-8x for 256-d vectors on AVX2.

7. Sparse graph representation

   • Dense adjacency matrix wastes memory for sparse brain graphs.
   • For Schaefer400, storing all 160K entries when only ~10K edges exist is wasteful.
   • Fix: Use compressed sparse row (CSR) format or petgraph's sparse graph.

8. Quantized embeddings for WASM

   • f64 embeddings are unnecessarily precise for browser-based applications.
   • Fix: Support f32 embeddings in WASM builds, halving memory and transfer size.

Long-term (v0.3+)

9. Streaming signal processing

   • Current design loads entire time windows into memory.
   • Fix: Implement ring-buffer based streaming for real-time operation.

10. GPU acceleration for large-scale spectral analysis

    • For Schaefer400 atlas, eigendecomposition of 400x400 matrices benefits from GPU.
    • Fix: Optional wgpu or vulkano backend for matrix operations.

ESP32 Constraints

Resource	Limit	Current Usage	Status
SRAM	520 KB	Ring buffer: 4096 x channels x 2 bytes = 8 KB (1 channel)	OK
SRAM (multi-channel)	520 KB	4096 x 16 x 2 = 128 KB (16 channels)	TIGHT
CPU	240 MHz dual-core	ADC sampling + data transmission	OK for 1 kHz
Flash	4 MB	Binary size with release profile	Needs measurement
WiFi throughput	~1 Mbps sustained	64 ch x 1000 Hz x 2 bytes = 128 KB/s = 1 Mbps	AT LIMIT

Recommendations:

• Use fixed-point arithmetic (i16 or Q15) instead of f64 on ESP32.
• Implement delta encoding or simple compression for data packets.
• Limit on-device processing to ADC readout and basic quality checks.
• Move all signal processing (FFT, connectivity, graph construction) to the host.
• Profile binary size with cargo bloat to ensure it fits in 4 MB flash.
• Consider reducing ring buffer size for multi-channel configurations.

Benchmarking Recommendations

Per-Crate Microbenchmarks (criterion)

# Add to each crate's Cargo.toml
[[bench]]
name = "benchmarks"
harness = false

[dev-dependencies]
criterion = { workspace = true }

Crate	Benchmark	Input Size	Metric
ruv-neural-signal	bench_hilbert_transform	256, 512, 1024, 2048, 4096 samples	ns/op
ruv-neural-signal	bench_compute_psd	1024, 4096 samples	ns/op
ruv-neural-signal	bench_plv_pair	1024 samples	ns/op
ruv-neural-signal	bench_connectivity_matrix	16, 32, 68 channels x 1024 samples	ms/op
ruv-neural-mincut	bench_stoer_wagner	10, 20, 50, 68, 100 nodes	us/op
ruv-neural-mincut	bench_spectral_bisection	10, 20, 50, 68, 100 nodes	us/op
ruv-neural-mincut	bench_cheeger_constant	8, 12, 16 nodes (exact), 32, 68 (approx)	us/op
ruv-neural-embed	bench_spectral_embed	20, 50, 68, 100 nodes	us/op
ruv-neural-memory	bench_brute_force_nn	100, 1K, 10K embeddings x 256-d	us/op
ruv-neural-memory	bench_hnsw_insert	1K, 10K embeddings x 256-d	us/op
ruv-neural-memory	bench_hnsw_search	1K, 10K embeddings, k=10, ef=50	us/op
ruv-neural-esp32	bench_adc_read	100, 1000 samples x 1-16 channels	us/op

Full Pipeline Profiling

# Generate a flamegraph of the full pipeline
cargo flamegraph --bench full_pipeline -- --bench

# Memory profiling with DHAT
cargo test --features dhat-heap -- --test full_pipeline

WASM Performance

// When ruv-neural-wasm is implemented, measure with:
performance.mark('embed-start');
const embedding = ruv_neural.embed(graphData);
performance.mark('embed-end');
performance.measure('embed', 'embed-start', 'embed-end');

ESP32 Hardware Timing

// Use esp-idf-hal's timer for hardware-level benchmarks
let start = esp_idf_hal::timer::now();
let samples = reader.read_samples(1000)?;
let elapsed_us = esp_idf_hal::timer::now() - start;

Performance Findings from Code Audit

PERF-001: Redundant Hilbert Transforms (Severity: High)

Location: ruv-neural-signal/src/connectivity.rs, compute_all_pairs

Each call to phase_locking_value, coherence, imaginary_coherence, or amplitude_envelope_correlation independently calls hilbert_transform on both input signals. In compute_all_pairs with 68 channels, each channel's analytic signal is computed 67 times.

Impact: For 68 channels x 1024 samples, this means 4,556 FFTs instead of 68. Estimated waste: ~98.5% of FFT compute in the connectivity matrix.

Fix: Pre-compute all analytic signals, then pass slices to pairwise metrics:

pub fn compute_all_pairs_optimized(channels: &[Vec<f64>], metric: &ConnectivityMetric) -> Vec<Vec<f64>> {
    let analytics: Vec<Vec<Complex<f64>>> = channels.iter()
        .map(|ch| hilbert_transform(ch))
        .collect();
    // ... use pre-computed analytics for all pair computations
}

PERF-002: O(n) Eviction in Memory Store (Severity: Medium)

Location: ruv-neural-memory/src/store.rs, evict_oldest

fn evict_oldest(&mut self) {
    self.embeddings.remove(0);  // O(n) shift
    self.rebuild_index();       // O(n) rebuild
}

For a store with 10K embeddings, every insertion at capacity triggers an O(n) shift and full index rebuild.

Fix: Use VecDeque<NeuralEmbedding> and maintain the index incrementally.

PERF-003: FFT Planner Re-creation (Severity: Medium)

Location: ruv-neural-signal/src/spectral.rs (lines 12-13), hilbert.rs (lines 25-27)

A new FftPlanner is created on every function call. rustfft caches FFT plans internally in the planner, but creating a new planner discards the cache.

Fix: Use a thread-local or static planner:

thread_local! {
    static FFT_PLANNER: RefCell<FftPlanner<f64>> = RefCell::new(FftPlanner::new());
}

PERF-004: Dense Adjacency for Sparse Graphs (Severity: Low)

Location: ruv-neural-core/src/graph.rs, adjacency_matrix

Always allocates an N x N matrix even when the graph has far fewer edges. For Schaefer400 with ~5K edges, this allocates 1.25 MB for a matrix that is ~97% zeros.

Fix: Return a sparse representation for large graphs, or provide both adjacency_matrix() and sparse_adjacency().

PERF-005: Power Iteration Convergence Not Checked (Severity: Low)

Location: ruv-neural-mincut/src/spectral_cut.rs, largest_eigenvalue

Runs a fixed 200 iterations regardless of convergence. Many graphs converge in 20-50 iterations.

Fix: Add early termination when eigenvalue change < epsilon:

if (eigenvalue - prev_eigenvalue).abs() < 1e-12 {
    break;
}

Note: fiedler_decomposition already has this check, but largest_eigenvalue does not.

---

Action Items

Critical (Must fix before v0.1 release)

• SEC-001: Add maximum size limits to RVF deserialization
• SEC-002: Validate sample_rate_hz > 0 and is_finite() in MultiChannelTimeSeries::new
• SEC-004: Fix integer overflow in AdcConfig::max_raw_value
• PERF-001: Pre-compute Hilbert transforms in compute_all_pairs

Important (Should fix before v0.1 release)

• SEC-003: Add NaN/Inf validation for signal data at pipeline entry points
• SEC-005: Add bounds check on HNSW entry point index
• PERF-002: Replace Vec::remove(0) with VecDeque in memory store
• PERF-003: Cache FFT planner across calls
• Add BrainGraph::validate() for edge index bounds and weight finiteness
• Add dimension consistency check to NeuralMemoryStore::store
• Remove unused workspace dependencies (petgraph, tokio, ruvector-*)

Recommended (Fix in v0.2)

• Implement zeroize-on-drop for NeuralEmbedding and NeuralMemoryStore
• Add strip_pii() to RvfFile
• Migrate Vec<Vec<f64>> matrices to ndarray::Array2<f64>
• Add Rayon parallelism for connectivity matrix computation
• Add criterion benchmarks for all crates
• Implement TDM protocol with CRC32 and node authentication
• Add cargo deny and cargo audit to CI
• Profile and optimize binary size for ESP32

Future (v0.3+)

• Encryption-at-rest for NeuralMemoryStore
• DTLS/TLS for ESP32 network protocol
• Sparse graph representation for large atlases
• f32 quantized embeddings for WASM
• Streaming signal processing pipeline
• GPU backend for large-scale spectral analysis

---

This document should be reviewed and updated after each milestone. All security findings should be verified as resolved before the corresponding release.