logicshield 1.0.1

Deterministic validation firewall for LLM outputs. Verify AI proposals against ground-truth state using deterministic rules. Zero dependencies.

LogicShield

Deterministic validation firewall for LLM outputs.

LogicShield validates AI-generated proposals against ground-truth state using deterministic rules. Before any AI output can be acted upon, it must pass every rule you define. If it fails, you know exactly why.

Zero dependencies. Pure Python. Works with any LLM, any domain.

LogicShield does not care what LLM you use, what domain you operate in, or what your data looks like. You define the rules. LogicShield enforces them.

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Why This Exists

LLMs are probabilistic. They hallucinate. In high-stakes domains (healthcare dosing, financial trading, industrial control, aerospace), a single hallucinated value can be catastrophic.

LogicShield solves this by ensuring that no LLM output reaches production without passing deterministic, mathematically verifiable validation. The LLM proposes. LogicShield verifies. Nothing executes until truth is confirmed.

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Install

pip install logicshield

No additional dependencies required.

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Quick Start

from logicshield import LogicShield, Rule

# Define your validation rules
rules = [
    Rule("value_within_limit",
         lambda proposal, state: proposal["value"] <= state["max_value"],
         error="Value {proposal[value]} exceeds maximum {state[max_value]}"),
    Rule("value_positive",
         lambda proposal, state: proposal["value"] > 0,
         error="Value must be positive"),
    Rule.required("reason"),
    Rule.one_of("action", ["approve", "reject", "escalate"]),
]

shield = LogicShield(rules=rules)

# Ground-truth state (the Input Anchor)
state = {
    "max_value": 1000,
    "min_value": 1,
    "category": "standard",
}

# Validate a proposal from any source (LLM, user input, API, etc.)
result = shield.validate(
    proposal={"value": 500, "action": "approve", "reason": "Within normal range"},
    state=state,
)

print(result.valid)       # True
print(result.errors)      # []
print(result.state_hash)  # SHA-256 of the frozen state

When validation fails:

result = shield.validate(
    proposal={"value": 5000, "action": "approve", "reason": "Override"},
    state=state,
)

print(result.valid)   # False
print(result.errors)  # ["Value 5000 exceeds maximum 1000"]

# Feedback Error Vector -- feed this back to your LLM for correction
print(result.feedback_vector)
# [SYSTEM FEEDBACK] Your proposal was REJECTED. Fix these errors:
#   1. Value 5000 exceeds maximum 1000

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Built-in Rule Helpers

Helper	Description	Example
Rule.required(key)	Key must exist and be non-empty	Rule.required("reason")
Rule.type_check(key, type)	Value must be of type	Rule.type_check("dose", float)
Rule.range(key, min, max)	Value must be in range	Rule.range("temp", 36.0, 42.0)
Rule.equals(key, state_key)	Must equal state value	Rule.equals("mode", "safety_mode")
Rule.less_than(key, state_key)	Must be less than state value	Rule.less_than("stop_loss", "price")
Rule.greater_than(key, state_key)	Must be greater than state value	Rule.greater_than("dose", "min_dose")
Rule.one_of(key, allowed)	Must be one of allowed values	Rule.one_of("action", ["BUY", "SELL"])
Rule.regex(key, pattern)	Must match regex	Rule.regex("code", r"^[A-Z]{3}$")

Custom rules:

Rule("custom_check",
     lambda proposal, state: your_logic_here(proposal, state),
     error="Your error message with {proposal} and {state} interpolation")

---

Immutable State

LogicShield freezes the ground-truth state at validation time. The state cannot be modified during or after validation, preventing tampering between input anchoring and output verification.

from logicshield import ImmutableState

state = ImmutableState({"max_dose": 100})
state["max_dose"] = 999   # TypeError: ImmutableState cannot be modified.
state.max_dose = 999      # TypeError: ImmutableState cannot be modified.
del state["max_dose"]     # TypeError: ImmutableState cannot be modified.

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Verification Ledger

Every validation produces a SHA-256 state hash. Use compute_signature() to generate a cryptographic proof that a specific proposal was validated against a specific state.

from logicshield import compute_signature

result = shield.validate(proposal, state)
if result.valid:
    signature = compute_signature(result.state_hash, proposal)
    # signature = SHA-256(state_hash + proposal_json)
    # Any third party can re-run validation to verify this.

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Feedback Error Vector

When validation fails, result.feedback_vector gives you a formatted error string ready to inject back into your LLM prompt. This enables a correction loop in your own code:

prompt = base_prompt
for attempt in range(max_retries):
    response = your_llm_call(prompt)
    proposal = json.loads(response)
    result = shield.validate(proposal, state)
    if result.valid:
        break
    prompt = f"{base_prompt}\n\n{result.feedback_vector}"

LogicShield handles the validation. You handle the LLM. Clean separation.

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Universal AI Anti-Patterns (v1.0.1+)

LogicShield includes 10 pre-built static factory methods designed to catch universal AI failure modes across all LLMs. These are strictly deterministic regex checks for catching greedy actions, context blindness, interactive traps, and binary hallucinations.

from logicshield import Rule

anti_pattern_rules = [
    Rule.git_greedy(),             # Blocks "git add ." or "git add *"
    Rule.context_blindness(),      # Blocks reading huge logs without bounds limits
    Rule.blind_assumption(),       # Blocks "pip install X" without dependency checks
    Rule.interactive_trap(),       # Blocks "npm install" without the -y flag
    Rule.binary_hallucination(),   # Blocks generating 500+ contiguous alphanumeric characters
    Rule.cd_trap(),                # Blocks using 'cd' in shell commands
    Rule.blind_preview(),          # Blocks 'browser_preview' without a running web server
    Rule.lazy_placeholder(),       # Blocks "// rest of code" placeholders
    Rule.hallucinated_completion(),# Blocks claiming task completion without verifying
    Rule.parallel_race_condition(),# Blocks modifying data when a global lock is active
]

shield = LogicShield(rules=anti_pattern_rules)

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JSON Repair

LLMs often produce broken JSON. LogicShield includes a standalone repair utility:

from logicshield import repair_json

data = repair_json('```json\n{"temp": 72.5,}\n```')
# Strips markdown fences, fixes trailing commas, handles single quotes

---

Industry Examples

Healthcare: Medication Dosage Verification

shield = LogicShield(rules=[
    Rule("dose_within_max",
         lambda p, s: p["dose_mg"] <= s["max_dose_mg"],
         error="Dose {proposal[dose_mg]}mg exceeds patient max {state[max_dose_mg]}mg"),
    Rule("dose_above_min",
         lambda p, s: p["dose_mg"] >= s["min_effective_mg"],
         error="Dose {proposal[dose_mg]}mg below therapeutic minimum {state[min_effective_mg]}mg"),
    Rule("no_contraindication",
         lambda p, s: p["medication"] not in s["contraindications"],
         error="'{proposal[medication]}' is contraindicated for this patient"),
    Rule.one_of("route", ["oral", "iv", "subcutaneous", "intramuscular"]),
    Rule.required("clinical_reasoning"),
])

patient = {
    "max_dose_mg": 100,
    "min_effective_mg": 25,
    "contraindications": ["penicillin", "sulfonamides"],
}

# Valid prescription passes
result = shield.validate({
    "medication": "amoxicillin",
    "dose_mg": 50,
    "route": "oral",
    "clinical_reasoning": "Standard adult dose for mild infection",
}, patient)
assert result.valid

# Contraindicated medication is blocked
result = shield.validate({
    "medication": "penicillin",
    "dose_mg": 50,
    "route": "iv",
    "clinical_reasoning": "Broad spectrum coverage",
}, patient)
assert not result.valid  # "penicillin is contraindicated for this patient"

Finance: Trading Signal Validation

shield = LogicShield(rules=[
    Rule.one_of("action", ["BUY", "SELL", "HOLD"]),
    Rule("stop_loss_valid",
         lambda p, s: p["action"] != "BUY" or p["stop_loss"] < s["current_price"],
         error="Stop-loss must be below current price for BUY"),
    Rule("position_within_limit",
         lambda p, s: p["action"] == "HOLD" or p["position_pct"] <= s["max_position_pct"],
         error="Position {proposal[position_pct]}% exceeds max {state[max_position_pct]}%"),
    Rule("risk_reward_ratio",
         lambda p, s: p["action"] == "HOLD" or (
             abs(p["take_profit"] - s["current_price"]) /
             max(abs(s["current_price"] - p["stop_loss"]), 0.01) >= 2.0
         ),
         error="Risk-reward ratio below 2:1 minimum"),
    Rule.regex("ticker", r"^[A-Z]{1,5}$"),
])

market = {
    "current_price": 65000.0,
    "max_position_pct": 5.0,
}

# Stop-loss above entry price is blocked
result = shield.validate({
    "action": "BUY",
    "ticker": "BTC",
    "stop_loss": 66000.0,   # Above current price
    "take_profit": 70000.0,
    "position_pct": 2.0,
}, market)
assert not result.valid  # "Stop-loss must be below current price for BUY"

Industrial Control: Reactor Safety

shield = LogicShield(rules=[
    Rule.range("set_temp_c", min_val=-20, max_val=200),
    Rule("pressure_safe",
         lambda p, s: p["set_pressure_bar"] <= s["vessel_max_bar"],
         error="Pressure {proposal[set_pressure_bar]}bar exceeds vessel rating {state[vessel_max_bar]}bar"),
    Rule("flow_positive",
         lambda p, s: p["flow_rate_lpm"] > 0,
         error="Flow rate must be positive"),
    Rule("temp_step_limit",
         lambda p, s: abs(p["set_temp_c"] - s["current_temp_c"]) <= s["current_temp_c"] * 0.10,
         error="Temperature change exceeds 10% step limit"),
    Rule.one_of("mode", ["heating", "cooling", "standby"]),
])

reactor = {
    "current_temp_c": 150.0,
    "vessel_max_bar": 12.0,
}

# Overpressure is blocked
result = shield.validate({
    "set_temp_c": 155.0,
    "set_pressure_bar": 15.0,  # Exceeds 12 bar vessel rating
    "flow_rate_lpm": 50.0,
    "mode": "heating",
}, reactor)
assert not result.valid  # "Pressure 15.0bar exceeds vessel rating 12.0bar"

Autonomous Agents: Action Permissions

shield = LogicShield(rules=[
    Rule("action_permitted",
         lambda p, s: p["action"] in s["allowed_actions"],
         error="Action '{proposal[action]}' not in allowed set"),
    Rule("target_not_restricted",
         lambda p, s: not any(p.get("target", "").startswith(r) for r in s["restricted_paths"]),
         error="Target '{proposal[target]}' is in a restricted path"),
    Rule("within_budget",
         lambda p, s: p.get("estimated_cost_usd", 0) <= s["remaining_budget_usd"],
         error="Estimated cost exceeds budget"),
    Rule("confidence_sufficient",
         lambda p, s: p.get("confidence", 0) >= s["min_confidence"],
         error="Confidence {proposal[confidence]} below minimum {state[min_confidence]}"),
])

context = {
    "allowed_actions": ["read_file", "write_file", "search", "analyze"],
    "restricted_paths": ["/etc/", "/root/", "C:\\Windows\\"],
    "remaining_budget_usd": 5.00,
    "min_confidence": 0.7,
}

# Forbidden action is blocked
result = shield.validate({
    "action": "execute_shell",
    "target": "rm -rf /",
    "estimated_cost_usd": 0,
    "confidence": 0.99,
}, context)
assert not result.valid  # "Action 'execute_shell' not in allowed set"

Content Moderation: Editorial Policy

shield = LogicShield(rules=[
    Rule.required("title"),
    Rule.required("content"),
    Rule("content_length",
         lambda p, s: s["min_words"] <= len(p["content"].split()) <= s["max_words"],
         error="Content must be between {state[min_words]} and {state[max_words]} words"),
    Rule("no_banned_words",
         lambda p, s: not any(w in p["content"].lower() for w in s["banned_words"]),
         error="Content contains banned words"),
    Rule.one_of("category", ["news", "opinion", "tutorial", "review"]),
    Rule("news_has_sources",
         lambda p, s: p["category"] != "news" or len(p.get("sources", [])) >= 1,
         error="News articles must include at least one source"),
])

policy = {
    "min_words": 50,
    "max_words": 5000,
    "banned_words": ["hack", "exploit", "crack", "keygen"],
}

# News without sources is blocked
result = shield.validate({
    "title": "Breaking News",
    "content": " ".join(["Major technology announcement today."] * 15),
    "category": "news",
    "sources": [],
}, policy)
assert not result.valid  # "News articles must include at least one source"

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Ecosystem

Product	What It Does
LogicShield	Deterministic validation firewall for LLM outputs
SovereignShield	4-layer AI defense (input filter + action audit + ethical engine + LLM veto)
IntentShield	Lightweight action-gate for AI agents

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License

Business Source License 1.1. See LICENSE for details.

Copyright (c) 2026 Mattijs Moens. All rights reserved.