LLM FFW

LLM FFW (LLM Fast Firewall) is a high-speed deterministic rule engine for scanning inputs entering and outputs leaving an LLM runtime. Its runtime uses only the Python standard library: no model judge, network calls, probabilistic classification, or third-party packages.

The default scanner ships a secure baseline: SecretsRule, input-only InvisibleCharactersRule, input-only UnicodeTagSmugglingRule, PaymentCardRule, PrivateKeyRule, and JWTTokenRule. It detects constrained credential formats, armored private-key blocks, and structurally credible compact JWTs; canonicalizes contextual U+200B token obfuscation and non-RGI Unicode tag runs; and redacts Luhn-valid payment-card candidates. Findings retain original-text spans and safe category metadata rather than matched values.

Usage

Production integrations should use LLMFirewall. Its balanced default redacts detected secrets in both directions while preserving flow:

from llm_ffw import LLMFirewall

def main() -> None:
    with LLMFirewall() as firewall:
        safe_prompt = firewall.sanitize_input(prompt)
        model_output = call_model(safe_prompt)
        safe_output = firewall.sanitize_output(model_output)

if __name__ == "__main__":
    main()

Create one facade during application startup and reuse it for every request. Call start() and close() from a long-lived application's lifecycle hooks; the context-manager form is convenient for scripts and batch jobs. Standalone programs must protect their entry point with if __name__ == "__main__": because the facade uses worker processes.

ContentBlockedError represents a configured block decision. FirewallUnavailableError safely combines saturation, timeout, lifecycle, and worker failures without retaining the submitted text or an internal exception chain. Hosts should reject rather than forward content when inspection is unavailable.

Applications that need safe audit metadata can request a structured result without dropping to the process-pool API:

result = firewall.sanitize_input_result(prompt)
safe_prompt = result.text
for finding in result.findings:
    record_metric(finding.rule_id, finding.action.value)

sanitize_output_result() provides the same contract for model output. These methods still raise ContentBlockedError and FirewallUnavailableError, so a returned result is always forwardable under the selected policy. It retains only policy-processed text rather than a second pre-inspection copy; an explicit audit policy can intentionally leave that text unchanged. Its representation always omits the text as an additional safeguard. Do not log result.text or recover matched content by slicing the original request with finding spans.

Firewall, ProcessScannerPool, and Scanner remain lower-level APIs for custom policy results, process orchestration, and detection-only evaluation.

from llm_ffw import ScanScope, Scanner

scanner = Scanner()
text = get_llm_input()  # Your application supplies the text.

findings = scanner.scan(text, scope=ScanScope.INPUT)
for finding in findings:
    print(finding.rule_id, finding.severity.value, finding.span)

safe_text = scanner.redact(text, findings)

Output rules use the same scanner with an explicit direction. Paired output rules can opt into normalized prompt context; rules that do not request it pay no normalization cost:

findings = scanner.scan(
    model_output,
    scope=ScanScope.OUTPUT,
    prompt_context=prompt,
)

Omitting scope preserves the input-scan default. The text and optional prompt context are each bounded by ScannerConfig.max_input_chars.

Do not log text or slice a finding's span for diagnostics. Findings contain a category-only redacted_preview suitable for reporting.

The default ScannerConfig.max_input_chars is 8,000,000 characters, providing headroom for a typical one-million-token text context. Token-to-character ratios vary by model, language, and content; callers should still set an explicit limit appropriate to their deployment and memory budget.

Default invisible-character canonicalization

InvisibleCharactersRule removes a U+200B zero-width-space run only when it is embedded between ASCII token characters, then rescans the cleaned input through every enabled rule. Clean ASCII requests retain a single scan. The rule is input-only and enabled by default. Applications can opt out:

from llm_ffw import LLMFirewall, ScannerConfig

firewall = LLMFirewall(
    scanner_config=ScannerConfig(enable_invisible_characters=False),
)

More than 64 contextual runs produces one bounded BLOCK recommendation. Balanced and strict policies enforce that block; audit or a custom policy may override it. Other format, joining, bidi, private-use, and unassigned characters are not removed.

Default Unicode tag canonicalization

UnicodeTagSmugglingRule removes invisible tag-character runs from input and rescans the cleaned text. It preserves the three RGI subdivision-flag tag sequences pinned by Unicode Emoji 17.0 and treats malformed, extended, or other tag runs as findings. Applications can independently opt out with ScannerConfig(enable_unicode_tag_smuggling=False). More than 64 relevant runs fails closed.

Deployment-defined banned substrings

An optional immutable BannedSubstringCatalog provides constrained substring or ASCII-word matching for input and output. It defaults to redaction and never accepts caller regex.

Opt-in JSON output validation

Applications that require a complete JSON response can enable the bounded, output-only JSONOutputRule:

from llm_ffw import JSONOutputConfig, LLMFirewall

firewall = LLMFirewall(json_output_config=JSONOutputConfig())

Malformed syntax, duplicate keys, non-standard non-finite constants, and configured resource-limit violations block by default. The rule does not extract JSON from Markdown or repair malformed output.

Opt-in unsafe URL inspection

Applications that pass model-produced URLs to users or downstream tools can enable bounded structural URL inspection:

from llm_ffw import LLMFirewall, UnsafeURLConfig

firewall = LLMFirewall(unsafe_url_config=UnsafeURLConfig())

UnsafeURLRule redacts dangerous schemes, embedded URL user-info, local or non-public IP targets, exact documented cloud metadata hostnames, and ambiguous authorities under balanced policy. It checks input and output by default; deployments can restrict its scopes. It performs no DNS, HTTP, reputation, model, or other network call.

Default payment-card inspection

Bounded Luhn-based payment-card inspection is enabled in both directions by default. Applications can customize its limits and scopes:

from llm_ffw import LLMFirewall, PaymentCardConfig

firewall = LLMFirewall(payment_card_config=PaymentCardConfig())

PaymentCardRule redacts structurally plausible 13–19 digit ASCII candidates under balanced policy. Luhn is a checksum, not proof that a card exists or is active. Applications that intentionally pass checksum-valid identifiers or test cards can opt out with ScannerConfig(enable_payment_cards=False).

Supported secret signatures

Armored private-key inspection is independently enabled by default for input and output. Balanced policy redacts complete blocks and safely contains malformed or oversized blocks. Applications can customize bounded limits with PrivateKeyConfig or explicitly opt out with ScannerConfig(enable_private_keys=False).

Compact JWT inspection is also enabled by default. It validates canonical Base64URL, unique-member JSON header/payload objects, algorithm/signature consistency, and JWT-specific type or registered-claim evidence before redaction. It does not verify signatures or trust claims. Applications can customize bounded limits with JWTTokenConfig or opt out with ScannerConfig(enable_jwt_tokens=False).

Built-in catalog 3.0.0 has 28 constrained signatures and 47 prefixes for OpenAI, GitHub, AWS, Anthropic, GitLab, Slack, Stripe, Hugging Face, Google, npm, PyPI, SendGrid, and Shopify.

Credential-shaped values are high-severity redact findings. Credential identifiers such as Twilio SK... API key SIDs are deliberately excluded because they cannot authenticate without a separate secret.

The rule does not attempt generic password or entropy detection.

Versioned signature catalogs

SecretsRule uses BUILTIN_SECRET_CATALOG by default. Applications can pin an in-memory SecretCatalog containing organization-specific signatures. Catalogs contain literal prefixes, ASCII character sets, and length bounds; they cannot contain executable callbacks or arbitrary regular expressions.

import string

from llm_ffw import LLMFirewall, SecretCatalog, SecretSignature

internal = SecretSignature(
    signature_id="acme.service_token",
    provider="acme",
    secret_type="service_token",
    prefixes=("acme_live_",),
    suffix_chars=string.ascii_letters + string.digits,
    min_suffix_chars=32,
    max_suffix_chars=32,
    boundary_chars=string.ascii_letters + string.digits + "_",
    source="internal://security/service-token-format",
)
additional_catalog = SecretCatalog(
    # This coordinate identifies the final built-ins-plus-Acme catalog.
    catalog_id="acme.secrets.with_llm_ffw_builtins",
    version="3.0.0+acme.1",
    signatures=(internal,),
)
firewall = LLMFirewall(additional_secret_catalog=additional_catalog)

The scanner never downloads or reads catalogs. The host application constructs and pins the catalog before scanning, making updates testable and rollbackable.

The facade construction above adds the application catalog to the built-ins. The extension's catalog_id and version identify the final effective built-ins-plus-application catalog.

The resulting catalog is copied to each worker during startup and worker recycling; it is not persisted or changed per request. Exact or nested built-in prefix overlaps are rejected instead of weakening built-in coverage. An advanced deployment that deliberately wants no built-ins must say so:

firewall = LLMFirewall(replacement_secret_catalog=complete_catalog)

The extension and replacement options are mutually exclusive. The earlier ambiguous secret_catalog= facade keyword is intentionally unsupported before the first release so an extension cannot silently disable built-in detection.

Clients can inspect a stable, immutable summary before startup:

capabilities = firewall.capabilities()
print(capabilities.rule_count)
print(capabilities.secret_catalog.signature_count)
print(capabilities.secret_catalog.providers)

The summary includes rule IDs, purposes, scopes, policy identity, catalog identity, counts, and provider names. It deliberately excludes literal secret prefixes, source references, suffix alphabets, and matching internals. Treat even this summary as operational metadata: a web service should expose only an explicitly approved subset through an authenticated administrative endpoint. Catalog creation is trusted startup configuration, not a per-request API.

Runtime catalog updates

Services that need zero-downtime catalog changes should keep one LLMFirewallManager and route scans through it:

from llm_ffw import LLMFirewallManager

manager = LLMFirewallManager()
manager.start()

# This is the complete desired application-extension snapshot, not a delta.
manager.reload(additional_secret_catalog=updated_additional_catalog)

safe_prompt = manager.sanitize_input(prompt)
safe_output = manager.sanitize_output(model_output)

Reload starts and verifies a new worker generation while the current generation continues serving. It then atomically directs new requests to the new generation, waits for requests already using the old generation, and closes the old workers. If candidate construction or startup fails, the old generation remains active. reload_builtin_catalog() removes application additions through the same safe generation transition.

Every update must provide a newly versioned full snapshot containing all desired application signatures, including previously configured ones. The manager does not apply incremental patches. During a reload, both worker generations briefly coexist, so deployments must reserve process and memory headroom. Reload is a trusted administrative operation and must never be exposed directly to ordinary request or tenant data.

Development and validation

Python 3.14.7 or a newer 3.14 patch is required. This workspace is validated with Python 3.14.7 using a local .venv:

py -3.14 -m venv .venv
.venv\Scripts\python -m unittest discover -s tests -v
.venv\Scripts\python benchmarks/bench_scan.py --size 1000000 --rounds 5
.venv\Scripts\python benchmarks/generate_synthetic_dataset.py --size 8000000
.venv\Scripts\python benchmarks/bench_concurrent_scan.py --size 8000000 --workers 4 --requests 8
.venv\Scripts\python benchmarks/bench_manager_reload.py --size 8000000 --workers 2 --concurrency 4 --reloads 4 --min-requests 16 --max-tasks-per-child 8

The deterministic generator makes one match per catalog prefix plus near-miss cases without LLM, network, provider, or random calls. Its manifest contains expected spans and a digest, not corpus values. Benchmarks report only duration, throughput, and finding counts; they never print scanned content. A release is not approved until the exact candidate commit passes both Windows and Linux CI.

Production process concurrency

ProcessScannerPool is the supported multi-core execution boundary. It validates worker execution before accepting traffic, uses a bounded in-flight queue, recycles workers, returns only safe Finding objects, detects broken workers, and shuts down deterministically.

from llm_ffw import ProcessScannerPool, ProcessScannerPoolConfig, ScanScope

pool = ProcessScannerPool(
    pool_config=ProcessScannerPoolConfig(
        max_workers=4,
        max_in_flight=8,
        max_tasks_per_child=1_000,
    )
)
pool.start()  # Application startup/readiness hook.
try:
    findings = pool.scan(text, scope=ScanScope.INPUT, timeout=5)
finally:
    pool.shutdown(cancel_pending=True)  # Application shutdown hook.

For a bounded service termination window, begin with shutdown() early enough to let active scans finish. Call terminate() at the grace deadline and reserve kill() for the final hard-stop deadline.

Advanced integrations using the pool directly should call pool.process(...) rather than pool.scan(...) to apply the configured policy. The balanced default redacts secret findings; STRICT_POLICY blocks secret-bearing input requests and AUDIT_POLICY reports without changing text. Blocking rejects one request and does not stop workers.

Create one long-lived pool or manager per service instance, not one per request. Map ProcessPoolSaturatedError to overload handling and treat a broken pool as unhealthy.

License

LLM FFW is licensed under the Apache License 2.0, including its explicit contributor patent grant and patent-litigation termination terms.