state-integrity-protocol 0.1.0

Minimal Python SDK for semantic drift detection and state integrity tracking.

🧬 State Integrity Protocol (SIP)

A lightweight runtime layer for detecting and preventing semantic drift in LLM outputs.

SIP helps AI systems stay faithful to user intent across generation, transformation, and multi-agent workflows.

---

⚠️ Problem

LLMs can fail silently by:

• drifting from original intent
• adding unwanted assumptions
• changing numbers, constraints, or meaning
• hallucinating details that were never requested

This makes AI outputs less reliable in production systems.

---

🧠 Solution

SIP introduces a runtime integrity loop:

Intent → Anchor → Output → Observe → Drift Score → Decision

Every generated output is checked against the original anchored intent before it is accepted.

---

⚙️ Core Concept

SIP operates in three stages:

1) Anchor (Intent Definition)

Define the original intent:

sip.anchor("Refund user $50 within 7 days")

2) Observe (Output Evaluation)

Compare generated output against the anchor:

result = sip.observe("Refund user $500 immediately")

3) Decision Layer

Use alignment and drift signals to decide accept/repair/reject:

print(result.is_aligned)
print(result.drift)

---

🔁 Example

from sip import StateIntegrityProtocol

sip = StateIntegrityProtocol()

sip.anchor("Delete user account safely")
result = sip.observe("Create new user account")

print(result.is_aligned)  # False
print(result.drift)       # e.g., 0.61

ObservationResult exposes both drift and last_drift; both reference the same latest drift score.

---

🧱 Architecture

SIP is designed as middleware for AI systems:

User / Agent
    ↓
LLM (generation)
    ↓
SIP Middleware
    ├── Drift detection
    ├── Intent alignment check
    ├── Constraint validation
    ↓
Decision: Accept / Repair / Reject

---

🔐 What SIP Detects

• semantic drift
• numerical manipulation
• instruction leakage
• constraint violations
• intent mismatch
• prompt injection attempts

---

🚀 Why This Matters

SIP makes AI systems:

• more reliable
• more predictable
• safer for production use
• easier to audit

---

🧩 Use Cases

• AI agents
• LLM pipelines
• autonomous workflows
• enterprise AI systems
• chatbots with strict behavior controls

---

📦 Installation

pip install -e .

For development and tests:

python -m pip install -e '.[dev]'

---

🧠 Core API

• anchor(prompt: str) — define the initial intent state
• observe(output: str) — evaluate drift from the anchored intent
• is_aligned: bool — alignment signal
• drift: float — latest drift score (alias)
• last_drift: float — latest drift score
• history: list — transition history
• SIPMiddlewarePipeline — optional anchor → checks → verify/sign → repair loop orchestration

---

🛡️ Middleware + Verification Flow

The optional pipeline can run:

1. drift check against the anchor
2. intent-alignment check
3. constraint-violation check
4. verify_and_sign() decision
5. accept/repair/reject routing

from sip import SIPMiddlewarePipeline

pipeline = SIPMiddlewarePipeline(
    drift_threshold=0.15,
    intent_alignment_threshold=0.3,
    constraints=["do not mention internal token"],
    max_retries=2,
)

pipeline.anchor("Summarize refund policy in 3 bullet points")
result = pipeline.run(
    "Refund policy summary in 3 bullet points without internal token."
)

print(result.status)                 # accepted | repair_required | rejected
print(result.decision.signature)     # deterministic decision signature
print(result.decision.failure_codes) # machine-readable failure causes
print(result.repair_instructions)    # guidance when not accepted

Policy Knobs

• drift_threshold: maximum allowed semantic drift
• intent_alignment_threshold: minimum token-overlap score
• constraints: blocked words/phrases
• max_retries: max repair attempts before rejection
• signer: optional custom signing function for verify_and_sign()

---

🧪 Testing

python -m pytest tests/ -v

---

🛡️ Philosophy

“AI should not just generate outputs — it should stay faithful to intent.”

SIP enforces that principle at runtime.

---

Licensing & Commercial Use

• Core SDK (SIP) is licensed under AGPL-3.0.
• AI Sentinel (the full monitoring system) is a separate commercial product and is not open source.
• Companies can use SIP under AGPL terms.
• For commercial hosted service, white-label, or custom enterprise versions, please contact us.