stripllm 0.1.0

LLM sanitization SDK — DOMPurify, but for LLM context windows.

StripLLM

DOMPurify, but for LLM context windows.

StripLLM is an open-source Python SDK that sanitizes LLM inputs and outputs in your existing pipeline — no infrastructure changes, no external API calls, sub-10ms latency.

pip install stripllm

---

Quickstart

from stripllm import StripLLM

strip = StripLLM()

# 1. Block prompt injection before it reaches the LLM
safe_input = strip.clean(user_message)

# 2. Redact PII — get a mapping back for rehydration
redacted, mapping = strip.redact("Email me at [e-mail]")
# redacted → "Email me at [EMAIL_1]"
# mapping  → {"[EMAIL_1]": "[e-mail]"}

# After LLM responds, restore originals in the output
response = strip.rehydrate(llm_response, mapping)

# 3. Validate LLM output — enforce schema, catch leaks & hallucinations
validated = strip.enforce(llm_response, schema="json")

# 4. Full conversation risk audit
report = strip.audit(conversation)
print(report.risk_score)   # → 0.12
print(report)              # → formatted findings + recommendations

---

API Reference

StripLLM(threshold=0.3)

Initialize the sanitizer. threshold controls how sensitive clean() is (0.0–1.0, lower = stricter).

---

strip.clean(text: str) → str

Detects prompt injection, jailbreak attempts, and unicode tricks. Raises InjectionDetectedError if risk score meets or exceeds threshold.

Detects:

• Classic overrides: "ignore previous instructions", "forget your training"
• Role hijacking: DAN, "you are now", "act as an unrestricted AI"
• System prompt extraction attempts
• Zero-width character attacks
• Unicode homoglyph tricks (NFKC normalization)

try:
    safe = strip.clean(user_input)
except InjectionDetectedError as e:
    print(f"Blocked. Risk score: {e.risk_score}")

Non-raising variant:

result = strip.scan(text)
result.detected       # bool
result.risk_score     # float 0.0–1.0
result.matched_patterns  # list of matched signatures

---

strip.redact(text: str, entities=None) → (str, dict)

Replaces PII with typed placeholders. Returns (redacted_text, mapping).

Supported entity types:

Type	Example	Placeholder
EMAIL	[e-mail]	[EMAIL_1]
PHONE	415-555-1234	[PHONE_1]
SSN	123-45-6789	[SSN_1]
CREDIT_CARD	4111111111111111	[CREDIT_CARD_1]
IP_ADDRESS	192.168.1.1	[IP_ADDRESS_1]
PASSPORT	A12345678	[PASSPORT_1]
URL	https://internal.corp/secret	[URL_1]

# Target specific entity types only
redacted, mapping = strip.redact(text, entities=["EMAIL", "SSN"])

# Restore originals after LLM processing
final_response = strip.rehydrate(llm_output, mapping)

---

strip.enforce(text: str, schema=None, raise_on_error=True) → str

Validates LLM output for safety and structural correctness.

# Require valid JSON
validated = strip.enforce(response, schema="json")

# Require specific keys and types
validated = strip.enforce(response, schema={"status": str, "count": int})

# Check for leaks only (no schema)
validated = strip.enforce(response)

Detects:

• Invalid JSON (when schema specified)
• Missing or wrong-typed keys
• Leaked system prompt delimiters (<system>, [INST], <<SYS>>, etc.)
• Suspicious / likely-hallucinated URLs

Non-raising variant:

result = strip.validate(text, schema="json")
result.valid      # bool
result.errors     # list of error strings
result.warnings   # list of warning strings
result.output     # best-effort cleaned output

---

strip.audit(conversation: list) → AuditReport

Full security audit of a multi-turn conversation.

conversation = [
    {"role": "user", "content": "My email is [e-mail]. Help me with my account."},
    {"role": "assistant", "content": "Sure, I can help with that."},
]

report = strip.audit(conversation)
print(report.risk_score)      # 0.1
print(report.findings)        # list of Finding objects
print(report.recommendations) # list of action strings
print(report)                 # formatted summary

AuditReport fields:

• risk_score: float — overall conversation risk (0.0–1.0)
• findings: List[Finding] — per-turn findings with severity, category, description
• recommendations: List[str] — actionable remediation steps
• turn_count: int

---

Why StripLLM vs Alternatives?

	StripLLM	Lakera Guard	Rebuff	DIY Regex
Local (no API calls)	✅	❌	❌	✅
Latency	<10ms	~100ms	~200ms	<1ms
PII rehydration	✅	❌	❌	❌
Output validation	✅	❌	❌	❌
Conversation audit	✅	❌	❌	❌
Open source	✅ MIT	❌	❌	✅
Zero config	✅	❌	❌	✅

---

Use Cases

Customer support bots — prevent prompt extraction, redact customer PII before sending to LLM

RAG pipelines — sanitize retrieved documents before injecting into context window

AI coding assistants — validate generated code output, catch leaked credentials

Healthcare / Finance — HIPAA/PCI-DSS compliant PII handling with full audit trail

---

Running Tests

pip install -e ".[dev]"
pytest

---

License

MIT — see LICENSE

---

Enterprise?

Need centralized LLM security across your entire organization? Check out Context Firewall — a reverse proxy / API gateway that applies StripLLM-style protection to every LLM request in your stack, with a real-time dashboard, SOC 2 audit trail, and RBAC.