Sentinel OS Core

Modular, offline-first cognitive operating system for synthetic intelligence. Designed for autonomous reasoning, persistent memory, and goal evolution in air-gapped or adversarial environments.

Author: Bradley R. Kinnard

---

Table of Contents

Start Here	System	Assurance	Ops
Why This Exists	Architecture	Verification Evidence	Installation
Quick Start	Features	Formal Verification	Configuration
Changelog	Roadmap	Privacy Guarantees	Testing
Contributing		Cryptographic Primitives	Benchmarks
		Security Architecture	Docker
		Limitations	License

---

Changelog

See CHANGELOG.md for full version history.

Current Version: 0.1.0-alpha

Recent highlights:

• Chatbot interface with Ollama LLM integration - Interactive REPL with belief extraction, contradiction detection, and goal-directed responses
• Reasoning agent - Full cognitive loop: perception → reasoning → action → memory
• Chain-of-thought reasoning - Intermediate thought generation before response
• Extended 1000+ episode benchmarks with raw log export
• Full belief-update-goal-evolve cycle demo
• Explicit per-module threat model documentation
• CONTRIBUTING.md with guidelines
• CI workflow with coverage and skip warnings

---

Why This Exists

Most AI systems assume cloud connectivity and treat security as an afterthought. Sentinel OS Core is built for scenarios where:

• Network access is unavailable or untrusted
• Code execution must be sandboxed and auditable
• Beliefs and goals must evolve deterministically
• All state changes must be cryptographically verifiable
• Privacy budgets must be formally tracked

This is not a chatbot framework. It is a cognitive substrate for autonomous systems.

---

Verification Evidence

Test Suite: 448 Passed, 12 Skipped

🧪 View Full Test Screenshot

Module	Tests	Coverage
tests/test_chatbot.py	45 passed	Ollama adapter, reasoning agent, chatbot REPL, integration
tests/test_verification.py	24 passed	State machine, invariants, property tests, termination
tests/test_formal_verification.py	45 passed	Formal checker, proof log, Hypothesis property tests
tests/test_privacy.py	23 passed	Budget accounting, Laplace/Gaussian mechanisms, clipping
tests/test_crypto.py	38 passed	Commitments, PQ signatures, Merkle trees, signed chains
tests/test_zk_proofs.py	25 passed	Pedersen commitments, Schnorr proofs, state transition proofs
tests/test_isolation.py	35 passed	Isolation levels, hard-fail semantics, threat models
tests/test_scalability.py	21 passed	Scale metrics, no hard caps, 10k+ operations
tests/test_meta_evolution.py	30 passed	Evolution engine, objectives, determinism, bounds
tests/test_world_model.py	31 passed	Simulation, counterfactual, hard-fail when disabled
tests/test_sandbox.py	19 passed	Backwards compatibility, pattern blocking
tests/test_*.py (other)	112 passed	Memory, sync, integration, belief ecology, goal collapse
Skipped	12	Firejail, Docker, liboqs hybrid mode (requires external deps)

All tests run with pytest tests/ -v. Execution time: ~15s.

Demo: All Systems Operational

Live execution of python demo.py demonstrating all 5 core features: verification layer, privacy budget tracking, ZK proof generation/verification, Merkle tree construction, and signed audit chains.

Benchmark Results

📊 View Full Benchmark Screenshots

Performance validated across 100 iterations per operation with statistical rigor (mean, std dev, P50/P95/P99 percentiles):

Operation	Mean	P95	Ops/sec	What It Measures
HMAC signing	0.0018ms	0.0018ms	559,932	Tamper-evident log signing speed
DP noise	0.0018ms	0.0020ms	567,791	Differential privacy noise generation
ZK proof gen	0.0665ms	0.0871ms	15,043	Zero-knowledge state transition proofs
Contradiction detect	0.0972ms	0.1203ms	10,291	Belief conflict detection
Merkle build	0.1327ms	0.1543ms	7,535	Cryptographic commitment tree
State digest	0.1799ms	0.1944ms	5,558	Full state hash computation
Belief insert	0.2208ms	0.3770ms	4,529	Verified belief insertion with trace
Belief update	0.3565ms	0.3864ms	2,805	Confidence update with invariant check

Crypto Benchmark Results

Operation	Mean (ms)	P95 (ms)	Ops/sec	What It Measures
Pedersen commit	15.32ms	16.10ms	65	Discrete-log commitment creation
Pedersen verify	15.20ms	15.59ms	66	Commitment opening verification
Schnorr prove	15.17ms	15.55ms	66	ZK proof of knowledge generation
Schnorr verify	17.52ms	18.01ms	57	ZK proof verification
State transition proof	61.19ms	62.60ms	16	Full transition proof (commit + Schnorr)
Ed25519 sign	0.031ms	0.047ms	32,382	Signature generation
Ed25519 verify	0.095ms	0.112ms	10,518	Signature verification
Merkle build (100)	0.144ms	0.190ms	6,928	Tree construction
HE encrypt	3.62ms	4.60ms	276	CKKS encryption
HE add	0.063ms	0.089ms	15,771	Ciphertext addition
HE decrypt	0.93ms	1.10ms	1,078	CKKS decryption

Benchmarks run with 100 iterations each. See python -m benchmarks.crypto_benchmark for live results.

End-to-End Benchmark Results

Real-world workload benchmarks measuring complete operations across all system layers:

Operation	Duration	Threshold	Ops/sec	What It Measures
Belief insertion (1k)	1.31ms	500ms	763,379	Sequential belief storage
Belief insertion (10k)	8.30ms	5000ms	1,204,520	Large-scale belief storage
Belief retrieval (10k)	2.21ms	100ms	4,519,656	Random access by ID
Episode recording (1k)	1.59ms	500ms	628,091	Episodic memory recording
Episode replay	120.17ms	500ms	832	Sample 100 from 10k episodes x100
Memory roundtrip (10k)	45.23ms	5000ms	44	Save + load 10k beliefs
Goal operations (1k)	0.03ms	1000ms	39,231,070	Goal hierarchy traversal
Contradiction detection	0.00ms	500ms	570,449	Detect conflicts in 1k beliefs
Audit chain verify (1k)	109.66ms	1000ms	9,119	Verify 1000-entry signed chain
State transitions (1k)	1535.26ms	2000ms	651	Verified state machine transitions
Meta-evolution (100 gen)	1.75ms	1000ms	57,678	Hyperparameter optimization
World model sim (1k)	91.29ms	100ms	10,954	Causal simulation steps
Formal verification (10k)	1.58ms	50ms	3,788	Verify 10k beliefs with 6 invariants

All thresholds passed. Run python -m benchmarks.e2e_benchmark for live results.

Benchmark Environment

All benchmarks above were run on the following system:

Component	Details
OS	Ubuntu 22.04.5 LTS (Jammy)
Kernel	6.8.0-90-generic (x86_64)
Hostname	rogue
CPU	Intel i5-11400F @ 2.60GHz (6 cores / 12 threads, turbo to 4.4GHz)
RAM	32GB (24GB available)
Swap	10GB
Disk	906GB NVMe, 118GB free (87% used)
GPU	NVIDIA RTX 5070 (12GB VRAM, driver 570.195.03)

Want to contribute benchmark results from other hardware?

We'd love results from ARM, Windows, macOS, or other setups. To submit:

1. Run: python -m benchmarks.e2e_benchmark --hardware (auto-logs your specs)
2. Open an issue with the benchmark-results label
3. Attach the generated JSON from data/logs/

This helps validate cross-platform performance and identify hardware-specific bottlenecks.

---

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        SENTINEL OS CORE                         │
├─────────────────────────────────────────────────────────────────┤
│  ┌───────────────────────────────────────────────────────────┐  │
│  │                  VERIFICATION LAYER                       │  │
│  │  ┌──────────────┐  ┌─────────────┐  ┌──────────────────┐  │  │
│  │  │ StateMachine │  │ Invariants  │  │ PropertyTests    │  │  │
│  │  └──────────────┘  └─────────────┘  └──────────────────┘  │  │
│  └───────────────────────────────────────────────────────────┘  │
│                               │                                 │
│  ┌────────────────────────────▼───────────────────────────────┐ │
│  │                    CORE ENGINE                             │ │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │ │
│  │  │ BeliefEcology│  │ GoalCollapse │  │ContradictionTracer│ │ │
│  │  └──────────────┘  └──────────────┘  └──────────────────┘  │ │
│  └────────────────────────────┬───────────────────────────────┘ │
│                               │                                 │
│  ┌────────────────────────────▼───────────────────────────────┐ │
│  │                   PRIVACY LAYER                            │ │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │ │
│  │  │BudgetAccount │  │DP Mechanisms │  │ SecureAggregator │  │ │
│  │  └──────────────┘  └──────────────┘  └──────────────────┘  │ │
│  └────────────────────────────┬───────────────────────────────┘ │
│                               │                                 │
│  ┌────────────────────────────▼───────────────────────────────┐ │
│  │                   CRYPTO LAYER                             │ │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │ │
│  │  │ ZK Proofs    │  │ PQ Signatures│  │ Merkle Trees     │  │ │
│  │  └──────────────┘  └──────────────┘  └──────────────────┘  │ │
│  └────────────────────────────┬───────────────────────────────┘ │
│                               │                                 │
│  ┌────────────────────────────▼───────────────────────────────┐ │
│  │                   ISOLATION LAYER                          │ │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │ │
│  │  │ TrustBoundary│  │ IsolationEng │  │ SecurityAudit    │  │ │
│  │  └──────────────┘  └──────────────┘  └──────────────────┘  │ │
│  └────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

---

Features

Core (Implemented)

Feature	Description	Location
Belief Ecology	Dynamic belief network with propagation and decay	core/belief_ecology.py
Goal Collapse	RL-based goal evolution with DP noise	core/goal_collapse.py
Contradiction Tracing	Automatic detection and resolution	core/contradiction_tracer.py
Reasoning Agent	Full cognitive loop: perception → reasoning → action → memory	core/reasoning_agent.py
Persistent Memory	Async I/O via aiofiles	memory/persistent_memory.py
Episodic Replay	LRU-based episode storage	memory/episodic_replay.py
Soft Isolation	Restricted builtins, blocked imports, explicit threat model	security/soft_isolation.py
HMAC Audit Logs	Tamper-evident logging	security/audit_logger.py

Interfaces (Implemented)

Feature	Description	Location
Ollama LLM Adapter	Local LLM inference via Ollama API with deterministic seeding	interfaces/ollama_llm.py
Chatbot REPL	Interactive command-line interface with belief/goal inspection	interfaces/chatbot.py
Belief Extraction	LLM-based extraction of beliefs from user input	core/reasoning_agent.py
Chain-of-Thought	Intermediate reasoning generation before response	core/reasoning_agent.py
State Persistence	Save/load agent state including beliefs, goals, episodes	core/reasoning_agent.py

Verification (Implemented)

Feature	Description	Location
Formal State Machine	Immutable state with transition tracking	verification/state_machine.py
Invariant Checker	Runtime invariant verification	verification/invariants.py
Property Testing	Randomized property verification	verification/properties.py
Trace Integrity	Cryptographic chain verification	verification/state_machine.py
Formal Checker	Mechanical invariant checking	verification/formal_checker.py
Proof Log	Reproducible verification artifacts	verification/proof_log.py

Meta-Evolution and World Models (Implemented)

Feature	Description	Location
Meta-Evolution Engine	Hyperparameter optimization with measurable objectives	core/meta_evolution.py
Objective Functions	BeliefCoherence, Efficiency, Composite	core/meta_evolution.py
World Model Interface	Abstract interface for causal simulation	core/world_model.py
Simple World Model	Numpy-based deterministic simulation	core/world_model.py
Counterfactual Analysis	What-if simulation for decision making	core/world_model.py

Privacy (Implemented)

Feature	Description	Location
Budget Accountant	Epsilon-delta tracking with hard caps	privacy/budget.py
Laplace Mechanism	Proven ε-DP noise	privacy/mechanisms.py
Gaussian Mechanism	(ε,δ)-DP noise	privacy/mechanisms.py
Secure Aggregation	DP-preserving aggregation	privacy/mechanisms.py
Clipping	L2 norm bounding for sensitivity	privacy/mechanisms.py

Cryptography (Implemented)

Feature	Description	Location
Commitments	Hash-based state commitments (NOT zero-knowledge)	crypto/commitments.py
Ed25519 Signatures	Standard digital signatures	crypto/pq_signatures.py
Signed Log Chains	Tamper-evident audit chains	crypto/pq_signatures.py
Merkle Trees	Batch commitment and verification	crypto/merkle.py
Authenticated Sync	Signed belief export/import with replay protection	interfaces/authenticated_sync.py

Optional (Requires Dependencies)

Feature	Dependency	Status	Location
Homomorphic Encryption	tenseal	Working	crypto/homomorphic.py
Neuromorphic SNN	brian2	Working (no mock mode)	neuromorphic/__init__.py
Firejail Sandbox	firejail	Working	security/soft_isolation.py

---

Installation

git clone https://github.com/moonrunnerkc/sentinel-os-core.git
cd sentinel-os-core
python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Optional Dependencies

# Homomorphic encryption
pip install tenseal

# Neuromorphic simulation
pip install brian2

# Physics simulation
pip install mujoco pybullet

# Post-quantum (when available)
pip install liboqs-python

---

Quick Start

Run the Demo

python demo.py

Run the Chatbot (Requires Ollama)

The chatbot provides an interactive interface with belief extraction, contradiction detection, and goal-directed responses.

Prerequisites:

1. Install and start Ollama: ollama serve
2. Pull a model: ollama pull llama3.2

# run the chatbot
python examples/chatbot_demo.py

# or with options
python examples/chatbot_demo.py --model llama3.2 --verbose

Chatbot Commands:

• /help - Show available commands
• /beliefs - List extracted beliefs
• /goals - Show active goals
• /stats - Display agent statistics
• /privacy - Check privacy budget status
• /quit - Exit

Demo Output

============================================================
SENTINEL OS CORE - DEMO
============================================================

[CRYPTO MODE]
  Signature algorithm: ed25519
  ZK proofs (Pedersen/Schnorr): enabled
  Homomorphic encryption: disabled
  liboqs-python: NOT installed
  tenseal: available

[1/6] Verification Layer...
      Trace integrity: True

[2/6] Privacy Layer...
      Budget remaining: 0.90 epsilon
      Noisy value: -4.6996 (original: 0.5)

[3/6] State Commitments...
      Commitment valid: True
      Transition type: demo_update

[4/6] Zero-Knowledge Proofs...
      Pedersen commitment valid: True
      Schnorr proof valid: True
      Belief count delta proof valid: True
      [SCOPE: Pedersen commitments + Schnorr proofs over discrete log]

[5/6] Merkle Tree...
      Root: efa1adf8f0e90aea64b96250a9fe43c8...
      Leaves: 4

[6/6] Signed Audit Chain...
      Chain valid: True
      Algorithm: ed25519
      Entries: 3

============================================================
ALL SYSTEMS OPERATIONAL
============================================================

---

Configuration

System Config

Edit config/system_config.yaml:

llm:
  backend: llama-cpp
  model_path: data/models/your-model.gguf
  temperature: 0.0
  seed: 42

verification:
  enabled: true
  check_invariants_on_transition: true
  property_test_iterations: 50

privacy:
  total_epsilon: 1.0
  total_delta: 1.0e-5
  composition_mode: basic

features:
  # requires brian2 - will fail if unavailable
  neuromorphic_mode: false

isolation:
  level: python
  timeout_seconds: 30
  use_firejail: false

Security Config

Edit config/security_rules.json:

{
  "use_firejail": false,
  "allowed_paths": ["data/"],
  "hmac_key_seed": 42,
  "seccomp_profile": "execve,ptrace",
  "audit": {
    "sign_logs": true,
    "use_merkle_chain": true
  }
}

---

Formal Verification

Proven Invariants

The verification module enforces these invariants at runtime:

Invariant	Description
I1: Confidence Bounded	Belief confidence ∈ [0, 1]
I2: Priority Bounded	Goal priority ∈ [0, 1]
I3: Goal Status Valid	Status ∈ {active, collapsed, abandoned}
I4: No Orphan Contradictions	Resolved contradictions may reference deleted beliefs
I5: Step Monotonic	Step counter never decreases

Termination Guarantee

For a belief set of size n, contradiction resolution terminates in at most n(n-1)/2 steps.

Property Testing

from verification.properties import PropertyTester

tester = PropertyTester(seed=42)
results = tester.run_all_properties(iterations=100)

for r in results:
    print(f"{r.property_name}: {'PASS' if r.passed else 'FAIL'}")

---

Privacy Guarantees

Budget Accounting

from privacy.budget import PrivacyAccountant, BudgetExhaustedError

# create accountant with strict budget
accountant = PrivacyAccountant(total_epsilon=1.0, total_delta=1e-5)

# track every DP operation
accountant.spend(0.1, mechanism="laplace", operation="belief_update")
accountant.spend(0.2, mechanism="gaussian", operation="goal_collapse")

# budget exhaustion raises exception
try:
    accountant.spend(0.8)  # exceeds remaining budget
except BudgetExhaustedError:
    print("Budget exhausted!")

# export audit report
report = accountant.export_audit_report()

DP Mechanisms

• Laplace: ε-differential privacy for exact answers
• Gaussian: (ε,δ)-DP for approximate answers
• Randomized Response: ε-DP for boolean values
• Exponential Mechanism: ε-DP for selection

---

Cryptographic Primitives

Zero-Knowledge Proofs (Pedersen + Schnorr)

from crypto.zk_proofs import (
    PedersenScheme,
    SchnorrProver,
    SchnorrVerifier,
    StateTransitionProver,
    StateTransitionVerifier,
    G, P, _mod_exp,
)

# Pedersen commitment (hiding + binding)
pedersen = PedersenScheme(seed=42)
commitment = pedersen.commit(100)
valid = pedersen.verify(
    commitment.commitment,
    commitment.value,
    commitment.blinding,
)

# Schnorr proof of knowledge
secret = 12345
public = _mod_exp(G, secret, P)
prover = SchnorrProver(seed=42)
verifier = SchnorrVerifier()
proof = prover.prove(secret, public)
valid, msg = verifier.verify(proof, public)

# State transition proof (e.g., belief count delta)
trans_prover = StateTransitionProver(seed=42)
proof, msg = trans_prover.prove_belief_count_delta(
    pre_count=10,
    post_count=15,
    declared_delta=5,
)
trans_verifier = StateTransitionVerifier()
valid, msg = trans_verifier.verify(proof)

State Commitments

from crypto.commitments import StateCommitment

commitment = StateCommitment(seed=42)

# commit to a state (NOT zero-knowledge - just hashing)
result = commitment.commit_state(
    state={"belief": 0.5, "goal": "active"},
    metadata={"step": 1}
)
print(result.commitment_hash)

# verify commitment later
valid = commitment.verify_commitment(result.commitment_hash, result.state_hash)

Signed Log Chains

from crypto.pq_signatures import generate_keypair, Signer, Verifier, Algorithm
import time

keypair = generate_keypair(Algorithm.ED25519)
signer = Signer(keypair)

# sign audit entries
entry1 = {"event": "start", "timestamp": time.time()}
sig1 = signer.sign(json.dumps(entry1).encode())

# verify signature
verifier = Verifier(keypair.public_key, keypair.algorithm)
valid = verifier.verify(json.dumps(entry1).encode(), sig1)

Authenticated Sync

from crypto.pq_signatures import generate_keypair, Signer, Algorithm
from interfaces.authenticated_sync import AuthenticatedSync

# create sync instances for two devices
keypair_a = generate_keypair(Algorithm.ED25519, key_id="device_a")
signer_a = Signer(keypair_a)
sync_a = AuthenticatedSync(signer_a, keypair_a)

keypair_b = generate_keypair(Algorithm.ED25519, key_id="device_b")
signer_b = Signer(keypair_b)
sync_b = AuthenticatedSync(signer_b, keypair_b)

# register each other's keys
sync_a.register_peer_key(keypair_b)
sync_b.register_peer_key(keypair_a)

# export beliefs with signature
beliefs = [{"id": "b1", "content": "test"}]
export = sync_a.export_beliefs(beliefs)

# import verifies signature and checks for replay
result, imported = sync_b.import_beliefs(export)

---

Testing

# fast tests (CI default)
make test

# with coverage report
make test-cov

# warn on skipped tests
make test-warn-skips

# property-based tests with Hypothesis
make property-test

Reproducing Tests

For Hypothesis-based tests, reproduce failures with:

pytest tests/test_formal_verification.py --hypothesis-seed=12345

Seeds are printed on failure. See tests/conftest.py for configuration.

Coverage

Coverage threshold: 80%. Run make test-cov to generate HTML report at htmlcov/index.html.

---

Extended Benchmarks (1000+ Episodes)

Validate long-running stability:

# run 1000 episode benchmark
make bench-extended

# or manually
python -m benchmarks.extended_benchmark --episodes 1000 --seed 42

Latest Results (seed=42, 1000 episodes, 10 beliefs/episode):

Metric	Value
Total Runtime	0.57s
Avg Cycle	0.48ms
P95 Cycle	0.82ms
P99 Cycle	1.21ms
Peak Memory	48.1MB
Beliefs Created	10,000
Contradictions Found	82
Goal Convergence	100%
Error Rate	0.00%

Raw logs saved to data/logs/extended_benchmark_*.json.

---

Benchmarks

from benchmarks import SentinelBenchmarkSuite

suite = SentinelBenchmarkSuite(seed=42)
results = suite.run_all()
print(suite.generate_report(results))

Sample output:

SENTINEL OS CORE BENCHMARK REPORT
============================================================
belief_insert:
  Mean:       0.0234 ms
  P95:        0.0412 ms
  Ops/sec:    42735.04

state_digest:
  Mean:       0.1523 ms
  P95:        0.2134 ms
  Ops/sec:    6566.12

---

Docker

docker build -t sentinel-os .
docker run --network none sentinel-os pytest tests/ -v

---

Security Architecture

📖 See docs/THREAT_MODELS.md for comprehensive per-module threat analysis.

Isolation Levels

The system provides explicit isolation levels with documented threat models. No level silently downgrades.

Level	Defends Against	Does NOT Defend Against	Requires
none	Nothing	Everything	Nothing
pattern_only	Accidental imports, obvious eval/exec	Obfuscation, runtime attacks	Nothing
python_sandbox	Basic injection, timeout enforcement	Interpreter exploits, pickle attacks	Nothing
firejail	Filesystem, network, syscalls	Kernel vulnerabilities	firejail binary
docker	Process, filesystem, resources	Container escapes	docker daemon

Hard-Fail Semantics

from security.isolation import Isolation, IsolationConfig, IsolationLevel, IsolationUnavailableError

# this will HARD-FAIL if firejail is not installed
try:
    config = IsolationConfig(level=IsolationLevel.FIREJAIL)
    isolation = Isolation(config)
except IsolationUnavailableError as e:
    print(f"cannot use firejail: {e}")
    # choose a different level or exit

Trust Zones

1. TRUSTED: Core logic, verified invariants, signed code
2. SEMI_TRUSTED: Validated user config, sanitized inputs
3. UNTRUSTED: External inputs, user code, network data

What Is Actually Verified

• Invariants are checked on every transition
• Privacy budget is never exceeded
• Traces are cryptographically chained
• Merkle roots are verifiable
• Signatures use real Ed25519 (or hybrid PQ when liboqs available)

What Is Best-Effort

• Python sandbox (bypassable by determined attacker)
• Pattern-based code blocking (not comprehensive)
• No isolation level provides security boundary against kernel exploits

---

Limitations

Limitation	Details
Sandbox escapes	Python isolation is not a security boundary
ZK proof scope	Pedersen/Schnorr over discrete log only, NOT SNARK/STARK
Ed25519 default	PQ (Dilithium/Hybrid) requires liboqs-python
HE requires tenseal	Homomorphic encryption disabled without tenseal
LLM reproducibility	Varies across hardware
Scalability	Tested to 10k beliefs
Neuromorphic	Requires brian2, no mock mode

Security Gaps (Future Work)

The following are acknowledged needs for production deployment, not yet implemented:

Gap	Status	Notes
External security audit	Not done	Required before production use
Bug bounty program	Not started	Needed for adversarial testing
WebAssembly isolation	Not implemented	Would provide stronger sandbox than Python
CVE tracking	Not established	Need formal vulnerability disclosure process
FIPS 140-2 compliance	Not verified	Crypto primitives need certification
Penetration testing	Not done	Need red team engagement

These are tracked in the roadmap for v1.0.

Future Work (Not Yet Implemented)

The following are recognized needs for production readiness but are not yet implemented:

Item	Status	Notes
External security audit	🔮 Planned v1.0	No third-party audit yet; use at your own risk
Bug bounty program	🔮 Planned	Will consider after v0.5 stabilization
WebAssembly isolation	🔮 Research	Potential future alternative to Python sandboxing
CVE tracking	🔮 Planned	Will establish formal vulnerability disclosure process
FIPS certification	🔮 Not planned	Crypto is research-grade, not certified

Until these are addressed, this project is suitable for research and prototyping, not production security-critical systems.

---

Implemented Features (Phase 1 Complete)

The following crypto features are now implemented with real algorithms:

Feature	Implementation	Status
ZK Proofs	Pedersen commitments + Schnorr proofs over discrete log	✅ Working
PQ Signatures	Hybrid Ed25519+Dilithium3 mode (requires liboqs)	✅ Available
Homomorphic Encryption	CKKS via TenSEAL for encrypted computation	✅ Working
Signed Audit Chains	Ed25519 (default) or hybrid mode	✅ Working
Authenticated Sync	Signed exports with replay protection	✅ Working

ZK Proof Scope Limitation

The ZK proofs are scoped to discrete-log based protocols:

• ✅ Pedersen commitments (hiding, binding, homomorphic)
• ✅ Schnorr proofs of knowledge
• ✅ State transition invariant proofs
• ❌ NOT SNARK/STARK (no general computation)
• ❌ NOT range proofs (requires bulletproofs)

---

Contributing

See CONTRIBUTING.md for guidelines.

Quick start for contributors:

git clone https://github.com/moonrunnerkc/sentinel-os-core.git
cd sentinel-os-core
python3.12 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
make test

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Roadmap

Current: v0.1.x (Alpha)

• Core belief/goal/contradiction engine
• Verification layer with invariants
• Privacy budget tracking
• ZK proofs (discrete-log scope)
• Extended benchmarks (1000+ episodes)
• Threat model documentation
• Core-only mode integration tests
• Chatbot interface with Ollama LLM integration
• Reasoning agent with cognitive loop
• Chain-of-thought reasoning

Coming Soon: User-Facing UI

A graphical user interface is in development to make Sentinel OS Core accessible to non-technical users. Stay tuned for updates.

Next: v0.2.x

• User-facing web UI for chatbot and system monitoring
• Modular extraction: Extract privacy.budget as standalone sentinel-privacy on PyPI
• Enhanced isolation (seccomp profiles)
• Range proofs (Bulletproofs)
• Consensus protocol stubs
• SMT-based invariant checking (z3-solver)

Future: v1.0

• Production hardening
• External security audit
• Stable API freeze
• Community governance

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License

MIT License. See LICENSE.

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Built for systems that must think alone, verify everything, and trust nothing.