AIWAF

AIWAF is a multi-runtime web application firewall toolkit for Python, Node.js, WebAssembly, and Java applications. It combines deterministic request controls with route-aware policies, reputation tracking, request logging, training pipelines, and optional Isolation Forest anomaly detection.

This is the single authoritative README for the monorepo and every published AIWAF package. Release workflows stage this file, the root LICENSE, NOTICE, and licenses/ into package artifacts when a registry requires package-local copies.

AIWAF is a defense-in-depth component. It does not replace secure application code, authentication and authorization, dependency patching, TLS, a reverse proxy, or a managed edge WAF.

Packages

The next local drafts add bounded Java SQL inspection, a JavaScript bodyParser helper, and atomic Python Redis rate buckets. They have not been published.

For custom Node HTTP servers or middleware registered before a framework's parser, call aiwaf.bodyParser() before AIWAF and before forwarding requests. It parses JSON and URL-encoded bodies, retains the original forwarding bytes, and rejects oversized bodies (default 64 KiB). Native Fastify/Hapi integrations must run AIWAF after body parsing and before their proxy handler.

Java SQL inspection defaults to security.sql_injection_mode: "block"; monitor records findings without denying requests and off disables it. AIWAF_SQL_INJECTION_MODE provides the environment override. Inspection uses the configured body byte limit and rejects over-budget inspection with 413. Previously learned benign keywords no longer block ordinary Java routes.

Redis rate-cache failures now produce generic HTTP 503 responses in JavaScript and Python rate-limit middleware. JavaScript defaults to AIWAF_RATE_CACHE_FAILURE_MODE: 'closed'; explicitly setting 'open' preserves availability at the cost of enforcement when the cache fails. Flask rejects invalid Redis configuration at initialization, and FastAPI rejects requests instead of silently selecting a process-local cache. Python rediss:// URLs are delegated to redis-py for TLS support when that driver is installed. Java uses Jackson 2.22.3 to address advisories affecting 2.22.2.

Python Flask/FastAPI Redis buckets now update atomically across workers. Django's native django.core.cache.backends.redis.RedisCache uses Redis transactions while preserving its existing serialized bucket values. Memory and other custom cache backends serialize updates within one process; use the supported Redis backends when sharing limits across workers.

Each package is versioned and released independently.

Python 1.1.4, Rust/Python 0.2.3, Node.js 1.1.2, WASM 0.2.2, and Java 1.3.3 are local release candidates; see the upgrade notes.

Supported framework integrations include Django, Flask, FastAPI, Express, Fastify, Hapi, Koa, NestJS, Next.js API routes, AdonisJS, Sails.js, Spring MVC, and Jakarta Servlet.

What AIWAF provides

• IP blacklist, exemption, reputation, expiry, and progressive-block handling
• Sliding-window rate limiting and flood detection
• Header, method, request-size, content-type, and parameter validation
• Static and learned malicious keyword detection
• Honeypot field and form-timing checks
• UUID probing and tamper detection
• GeoIP allow/block policies using the bundled MMDB data
• Route manifests, path-specific overrides, and decorator/annotation exemptions
• Memory, file, CSV, SQLite, database, and cache-backed runtime state, depending on the runtime
• Structured request logging and offline training
• Six-feature anomaly models with Rust, JavaScript, and Java implementations
• Required Rust runtime for Python 1.1.4 and optional WebAssembly acceleration for Node.js

The runtimes share policy intent and training feature names, but their framework adapters and model implementations remain language-native. Model artifacts are not generally interchangeable across languages.

Repository layout

.
├── py/aiwaf/                       Python package and adapters
├── js/                             Node.js package and adapters
├── rust/                           Rust core and Python bindings
│   └── crates/aiwaf_wasm/          WebAssembly bindings
├── java/                           Java 17 package
├── tests/                          Python integration and parity tests
├── benchmarks/                     Python middleware benchmarks
├── examples/                       Focused Python examples
├── .github/workflows/              Tests, security checks, and releases
├── README.md                       Canonical project documentation
└── LICENSE                         Canonical Elastic License 2.0 (see NOTICE)

Quick installation

Choose the package for the application runtime:

# Python core or a framework extra
pip install aiwaf
pip install "aiwaf[django]"
pip install "aiwaf[flask]"
pip install "aiwaf[fastapi]"

# Backward-compatible alias; Python 1.1.4 already installs aiwaf-rust
pip install "aiwaf[rust]"

# Node.js middleware
npm install aiwaf

# Standalone WebAssembly bindings
npm install aiwaf-wasm

Java 1.3.3, after it is published to Maven Central:

<dependency>
  <groupId>io.github.aiwaf-project</groupId>
  <artifactId>aiwaf-java</artifactId>
  <version>1.3.3</version>
</dependency>

Python package

The Python package requires Python 3.10 or newer. Framework dependencies are optional, so install only the extra needed by the application.

Python 1.1.4 requires aiwaf-rust>=0.2.3; the rust extra remains an installation alias. Publish the matching Rust wheel with NativeCSVPipeline support before the Python release. The Rust release workflow builds Linux x86_64/aarch64, musl x86_64, macOS x86_64/arm64 and Windows x86_64 wheels and checks the native pipeline API. Windows and WSL builds are checked locally; the other wheel targets must pass CI before publication.

Rust-owned CSV pipeline

The complete CSV request pipeline is available through explicit adapters below. Install it instead of the existing eight-middleware chain. The existing AIWAF adapters remain available for compatibility with custom stores and hooks; they do not switch automatically to this pipeline.

Rust owns the immutable configuration, enforcement order, SQL and keyword inspection, geo and blacklist decisions, rate buckets, form timing state, UUID scores, anomaly inference/history, keyword learning and CSV logging. Python supplies framework routing/authentication facts, obtains GeoIP country data, buffers a bounded body prefix and constructs responses. Each request crosses into the engine once before the handler and once when its response is available. The engine never calls Python policy callbacks or falls back to Python execution. Missing native support, unsupported settings and invalid CSV schemas fail at startup.

# Flask
from flask import Flask
from aiwaf.flask import NativeAIWAF

app = Flask(__name__)
NativeAIWAF(app, config={"data_dir": "aiwaf_data"})

# FastAPI / pure ASGI
from fastapi import FastAPI
from aiwaf.fast import NativeAIWAFMiddleware

app = FastAPI()
app.add_middleware(NativeAIWAFMiddleware, config={"data_dir": "aiwaf_data"})

# Django settings: replace the existing AIWAF middleware entries
AIWAF_CSV_PIPELINE = {"data_dir": "aiwaf_data"}
MIDDLEWARE = [
    # Keep the application's other middleware in the required order.
    "aiwaf.django.csv_pipeline.NativeAIWAFMiddleware",
]

The configuration uses a dedicated lowercase profile; existing uppercase framework settings, AIWAFConfig dictionaries, PATH_RULES, manifests, custom stores and policy callbacks are not imported into it. Unknown keys are rejected. Route decorators for full/partial exemptions and required middleware are read once. Required decorators override decorator exemptions; profile-level path/IP exemptions remain global. The profile is immutable: construct a new engine and reload workers to change it.

Common profile keys and defaults:

Pass a trained aiwaf_rust.IsolationForest as model= to Flask/FastAPI, or set AIWAF_CSV_PIPELINE_MODEL in Django. Without a model, the anomaly stage maintains history and learns suspicious 404 keywords but performs no model-based blocking. pipeline.native.set_model(model) replaces the model with an owned snapshot; set_model(None) disables inference. Training and management APIs remain available through the existing Python interface.

For deployments that accept per-worker form timing, set "honeypot_backend": "memory" in the native profile. Rust then holds GET timestamps in an expiring map and evaluates/clears POST timing atomically, avoiding a durable CSV rewrite on each GET. Timing state is isolated per engine, is not shared across workers and resets on restart. Backend switches do not transfer timestamps automatically. Keep csv for shared or persistent timing; CSV writes remain immediately synced. Blacklist, keyword, geo and whitelist policy files and immediate request logging keep their configured CSV behavior.

A second matched WSL benchmark compared these two timing backends with all eight stages enabled. Three alternating runs of 300 requests per mode/framework measured the following added overhead after subtracting framework-only latency:

All 13,500 measured requests returned 200 and both protected modes blocked the SQL/PHP probes. These are medians of per-run mean server times for a synthetic one-client workload with immediate logging, 80% GET / 20% JSON POST, 30 fields and a seeded 100-tree Rust model when AI is enabled. The performance comparison does not remove the persistence and cross-worker differences between backends. Reproduce memory mode with --pipeline --honeypot-backend memory in the HTTP benchmark harness; the default remains --honeypot-backend csv.

Flask/FastAPI accept synchronous country_lookup(ip) and authenticated(request) callbacks for adapter facts. Default GeoIP uses the bundled lookup. Configure authentication explicitly in these adapters when authenticated requests should bypass honeypot timing; Django reads user/session state, so place it after authentication/session middleware. Django function views have no standard method declaration and are accepted conservatively; class-based views supply their declared method facts. Anomaly history stores route eligibility as observed at arrival; historical requests are not re-resolved after routes change.

The ASGI adapter preserves streaming bodies and finalizes enforcement/logging at response start. Logged response time measures time to response headers rather than stream completion. WSGI adapters finalize when the framework response is available. Session/route handlers, response construction, GeoIP lookup and legacy custom integrations still execute in Python; the whole Python package has not been rewritten in Rust.

Measure this path with scripts/benchmark_release_http.py --pipeline, alongside the existing chain and --plain framework baseline. Native execution and both SQL/PHP protection probes are checked by the harness; the benchmark report records workload details and subtracts the framework baseline for added overhead.

Matched WSL tests of the complete pipeline (three alternating runs of 300 requests per framework/mode, one client, all eight stages and immediate CSV logging) measured the following added overhead, after subtracting the same framework-only baseline. The comparison is against the current legacy chain.

These are medians of per-run mean server times. Native and legacy run ranges were separated in all six protected comparisons. All 13,500 measured requests returned 200 and both protected modes blocked SQL/PHP probes. The AI fixture used a seeded 100-tree Rust forest and 200 initial history records per client. The dedicated profile, storage layout and adapter semantics differ from some legacy configurations; this is a synthetic comparison of the documented modes, not a production latency guarantee or a drop-in migration result.

Flask

from flask import Flask
from aiwaf.flask import AIWAF

app = Flask(__name__)
AIWAF(
    app,
    middlewares=["auto"],
)

@app.get("/")
def home():
    return {"protected": True}

middlewares=["all"] enables the canonical middleware set. auto starts from the same set and omits checks that are not useful for the detected application signals.

FastAPI

from fastapi import FastAPI
from aiwaf.fast import AIWAF

app = FastAPI()

AIWAF(
    app,
    middlewares=["auto"],
    storage={"backend": "memory"},
    header_validation={"enabled": True, "quality_threshold": 3},
    rate_limiting={"enabled": True, "window_seconds": 10, "max_requests": 20},
    logging_middleware={
        "enabled": True,
        "log_dir": "aiwaf_logs",
        "log_format": "json",
    },
)

@app.get("/")
async def home():
    return {"protected": True}

Django

Add the app and the unified middleware alias in settings.py:

INSTALLED_APPS = [
    # ...
    "aiwaf.django",
]

MIDDLEWARE = [
    "django.middleware.security.SecurityMiddleware",
    "aiwaf.django.middleware.all",
    # ...
]

For explicit ordering, replace the alias with the individual middleware classes:

MIDDLEWARE = [
    "aiwaf.django.middleware.JsonExceptionMiddleware",
    "aiwaf.django.middleware.GeoBlockMiddleware",
    "aiwaf.django.middleware.IPAndKeywordBlockMiddleware",
    "aiwaf.django.middleware.RateLimitMiddleware",
    "aiwaf.django.middleware.AIAnomalyMiddleware",
    "aiwaf.django.middleware.HoneypotTimingMiddleware",
    "aiwaf.django.middleware.UUIDTamperMiddleware",
    "aiwaf.django.middleware.HeaderValidationMiddleware",
    "aiwaf.django.middleware_logger.AIWAFLoggerMiddleware",
]

Protection middleware should run early. Logging should run late enough to observe the application response.

Route manifests

aiwaf init imports the live application and records all registered routes in .aiwaf/paths.json. The --app value must be module:object, not only a module name.

# Flask application object
aiwaf init --framework flask --app app:app

# Flask application factory
aiwaf init --framework flask --app app:create_app

# FastAPI
aiwaf init --framework fastapi --app app:app

# Django
aiwaf init --framework django --settings project.settings

# Custom output
aiwaf init --framework flask --app app:app --output .aiwaf/paths.json

Run the command in the same environment and checkout used to start the application. If it discovers only / and Flask's static route, verify the imported file and live route table:

python -c "import app; print(app.__file__); print(*sorted(r.rule for r in app.app.url_map.iter_rules()), sep='\n')"

Python route exemptions

Django, Flask, and FastAPI expose equivalent route helpers:

from aiwaf.flask import (
    aiwaf_exempt,
    aiwaf_exempt_from,
    aiwaf_only,
    aiwaf_require_protection,
)

@app.get("/health")
@aiwaf_exempt
def health():
    return {"ok": True}

@app.post("/webhook")
@aiwaf_only("header_validation")
def webhook():
    return {"accepted": True}

Canonical middleware names include header_validation, rate_limit, ip_keyword_block, honeypot, geo_block, uuid_tamper, ai_anomaly, and logging.

Python configuration

Python adapters use flat AIWAF_* settings or their equivalent constructor options.

When aiwaf_rust is importable, supported acceleration paths are selected automatically. Python remains the fallback when a native capability is unavailable. Persisted runtime models use JSON state; AIWAF does not load pickle, joblib, or other executable Python-object formats.

Rust-backed CSV runtime and training

The Python 1.1.4 and aiwaf-rust 0.2.3 release candidates add native CSV storage, shared policy evaluation, and training preparation for Django, Flask, and FastAPI. Install matching Python and Rust builds and reload application workers to activate the new capabilities. Older extensions retain the compatible Python fallback.

• CSV storage: bounded snapshot caches and indexed IP/key lookups avoid repeated parsing and copying entire tables into Python. Native readers validate file metadata and use shared OS locks; mutations use exclusive locks. Blacklist reputation updates, keyword increments/removals, exemptions, audit entries, and geo-list changes use atomic transactions. Unchanged policy rows skip rewriting and disk synchronization. Changed policy writes still synchronize to disk.
• Request policies: native helpers handle keyword/probe decisions, ASCII SQL signatures, geo decisions, rate-window filtering, form timing, UUID format validation, reputation scoring, wildcard/CIDR exemptions, and longest-prefix route plans. Flask CSV keyword retrieval, evaluation, and learning share one native call. SQL checks and blacklist updates retain their existing order; custom evaluators and unsupported Unicode behavior use the Python adapter.
• Exemption reuse: Flask reuses CSV exemption results within a request and checks file identity, size, timestamps, and configuration before each reuse. Changes invalidate the result; decorator checks still execute. Set AIWAF_CSV_REQUEST_CACHE=False to disable this optimization.
• Training ingestion: plain and gzip CSV stream directly in bounded batches, including quoted fields and embedded newlines. Seconds and millisecond response fields are normalized. Apache logs use batched extraction and native timestamp parsing for supported English/C-locale forms. Unsupported dates/locales and Unicode keyword learning retain Python behavior. Route checks are reused for distinct paths, and learned keywords are persisted in batches.
• Native model preparation: NativeTrainingBatch keeps features and numeric matrices in Rust through Isolation Forest fitting, prediction, and per-IP anomaly aggregation. NativeTrainingBuilder preserves Django's chunk/state behavior. Supported native model paths train without a Pandas DataFrame; older APIs and custom parsers retain compatibility paths.
• Synchronous CSV logging: a bounded cache of writer handles avoids repeated opens. Every row is locked and flushed before returning. File identity checks handle rotation/replacement, and truncation recreates the header. Rows are not queued in an application-side batch.

Framework hooks, request/body extraction, route discovery, session/ORM access, response construction, datetime object/epoch conversion, training orchestration, and model artifact serialization still run in Python. The route plan selects middleware; framework hooks continue to execute separately in their established order. Database and Redis backends remain separate from the CSV implementation.

Flask and Django honor AIWAF_USE_RUST=False or AIWAF_RUST_CSV=False to disable native CSV dispatch. Outside those application contexts, use the environment variable AIWAF_RUST_CSV=false. FastAPI training also honors these application options. ISO timestamps use Python's faster C parser by default; AIWAF_RUST_ISO_DATES=1 opts into native ISO parsing for measurement.

Flask can opt into shared, persistent CSV rate buckets:

app.config["AIWAF_RATE_CACHE_BACKEND"] = "csv"
# Optional: otherwise stored under AIWAF_DATA_DIR.
app.config["AIWAF_RATE_CSV_PATH"] = "/absolute/path/to/rate_buckets.csv"

CSV rate decisions trim the window, consume the request, and persist the bucket under one native lock. Missing or disabled native support returns HTTP 503. Memory remains the default; CSV rewrites and synchronization can be slower than memory or Redis. Workers sharing CSV state must use compatible schemas and a filesystem with suitable locking. Legacy Python fallback writers on Windows only have process-local thread locks, so mixed Python/native writers need particular care. Explicit whole-file replacements are not concurrent incremental updates.

AIWAF_CSV_LOG_FSYNC=True adds a disk sync per log row; the default is False, matching previous Python logging durability. Without per-row sync, a system crash can lose recently flushed OS-buffered entries. Cached handles close at process shutdown. After requests stop, call the Flask logger's close() or aiwaf.core.native_runtime.close_csv_logs() before deleting temporary log directories, especially on Windows.

Check the installed extension without touching application data:

python scripts/check_native_csv_runtime.py

Development request batching adds policy_sql_signatures and csv_geo_policy capabilities. Shared SQL inspection checks the first eight ASCII strings immediately for prompt denial, then checks subsequent strings in batches of at most 128. Unicode boundaries and inspection-limit exits flush pending checks first, preserving first-finding order. Django, Flask, and FastAPI use the shared path; older extensions retain single-string checks.

Flask with CSV geo state and an explicit allow/block list can read dynamic countries and evaluate the geo policy in one native call. Django CSV and FastAPI CSV use csv_runtime_geo_policy for their key/value geo state. Django additionally retains its existing ORM country blocks; the combined call includes those countries as extra policy input. ORM-only Django operation remains separate. FastAPI geo middleware honors its use_rust option, passed from the application's Rust flags. Dynamic-only FastAPI policy and custom getters/evaluators retain their fallback path. Native reads respect TTLs and observe updates/removals; disabled native geo reads refresh the Python CSV snapshot. Exemptions and blacklist writes still run at their original stages. Rebuild the extension from this checkout to test these unpublished capabilities.

Run python scripts/benchmark_native_request_batches.py for the focused comparison. Three alternating Windows runs of 500 checks measured 100-field SQL inspection at 0.650 ms combined versus 0.989 ms previous, and the CSV geo binding at 0.286 ms versus 0.367 ms. Early SQL rejection measured 0.067 ms versus 0.066 ms. These isolated, warmed checks exclude the complete middleware chain, networking, model scoring, and concurrent load.

Request-time anomaly acceleration (development)

With a matching rebuilt extension and a real Rust Isolation Forest, Flask and FastAPI retain request histories as immutable NativeAnomalyHistory snapshots. Native code trims the time window, builds the six current features, and predicts without exporting the numeric matrix. Records are shared between snapshots; evaluating a response does not mutate another request's history. Existing per-worker cache ownership and response/keyword/blocking order remain unchanged.

If the forest predicts an anomaly, Python still resolves the current route and exemptions for each historical path. Rust then analyzes the retained records. Dynamic route/exemption changes remain visible. Unicode paths/keywords, non-finite current inputs, disabled native settings, older extensions, and other model types retain the established Python-history path. Set AIWAF_RUST_ANOMALY_HISTORY=False in Flask config or the FastAPI AIWAFConfig object to disable native history retention; existing snapshots convert back to tuple lists. Disabling or replacing a model also retains compatibility.

Django continues to cache serializable tuple lists, including with shared cache backends. The shared recent-behavior bridge uses analyze_recent_tuples to avoid building a dictionary per historical record. No native process-local object is stored in Django's cache. These changes do not introduce model auto-reload or shared histories for Flask/FastAPI.

Run python scripts/benchmark_anomaly_runtime.py to compare the previous bridge, tuple analysis, and native histories with a real seeded forest. Five alternating Windows runs of 300 evaluations, a 100-tree forest, and 200 prior requests measured anomalous evaluation at 0.175 ms versus 0.486 ms (2.78x) and benign evaluation at 0.040 ms versus 0.146 ms (3.61x). The same WSL benchmark measured anomalous evaluation at 0.178 ms versus 0.533 ms (2.99x) and benign evaluation at 0.036 ms versus 0.126 ms (3.46x). These isolated engine timings exclude networking, CSV writes, real route-resolution costs, the full middleware chain, and concurrent load. They are not deployed-site speedup claims.

Further request-path refinements use the existing native CSV log writer for Flask/FastAPI anomaly training logs when supported. Each row is flushed before returning; there is no background queue. AIWAF_CSV_LOG_FSYNC=True additionally requests an OS sync for these logs, including in the Python fallback. Rust-disable settings and older extensions retain the Python writer.

Flask reuses its routing adapter within a request while continuing to match each path and evaluate exemptions. Route-map replacement and additions remain visible; the MethodNotAllowed import was corrected so existing routes with another HTTP method are recognized. Flask/FastAPI sweep inactive histories at most every 30 seconds during response processing, removing only histories with no entries in the configured scoring window. Active histories remain uncapped, and a quiet worker performs no background cleanup. Django continues to use cache timeouts.

Rust recent-behavior analysis now counts symmetric ten-second bursts with a linear sweep after sorting finite timestamps, replacing two binary searches per record. Non-finite timestamps retain the established search path. This shared-core change also applies to the WASM recent-behavior API. Run python scripts/benchmark_request_refinements.py for isolated logging, route, and burst-analysis timings; these do not measure deployed-site throughput. With five runs of 300 operations in WSL, immediate CSV writes measured 0.030 ms versus 0.173 ms, repeated route checks 0.0175 ms versus 0.0266 ms, and analysis of 1,000 history records 0.663 ms versus 0.787 ms. The route comparison uses the previous implementation with its exception import corrected; burst timings include Python-to-Rust tuple conversion. These benchmarks use local files and synthetic inputs, without concurrent load.

Python operations

Experimental compiled request stage

AIWAF_RUST_REQUEST_ENGINE=True opts into NativeRequestEngine with a matching rebuilt Rust extension. Set it in Flask/Django settings or the FastAPI AIWAFConfig. It is off by default. This first stage combines bounded ASCII query SQL inspection, default keyword-context evaluation, dynamic CSV keyword reads, and keyword learning writes behind a compiled policy profile. Static policy changes rebuild the profile; CSV state remains live. Flask/FastAPI can use one native call for supported query requests. Django keeps SQL inspection before route resolution, then uses the compiled keyword/CSV stage with the resolved route facts.

Framework extraction, body buffering/replay, exemptions, blacklist enforcement, rate limits, honeypot/UUID hooks, header enforcement, and response processing keep their existing positions. Structured SQL bodies, Unicode paths/decoding, monitor mode, custom policy/storage hooks, custom Django converters, older extensions, and dynamic-keyword subsets whose ranking requires Python retain the established path. The engine does not replace the whole eight-middleware chain.

Three alternating WSL runs of 300 requests through the full chain, at concurrency one with AI scoring off, observed server-time medians of 12.88 to 12.46 ms for Flask, 16.09 to 15.60 ms for Django, and 13.41 to 13.17 ms for FastAPI. Run ranges overlapped; these do not establish a dependable speedup. The opt-in default avoids assuming that fewer boundaries alone remove the remaining request cost. Compare scripts/benchmark_release_http.py with and without --engine using identical dependencies and traffic. --plain measures an endpoint without AIWAF, and the harness reports server processing time separately from HTTP latency.

# Auto-detected CLI (one supported framework installed)
aiwaf --help
aiwaf blacklist migrate
aiwaf status

# Django management commands
python manage.py detect_and_train
python manage.py regenerate_model
python manage.py aiwaf_reset --keywords --confirm
python manage.py aiwaf_logging --status
python manage.py geo_block_country list

# Explicit adapter dispatch
aiwaf django aiwaf_list --all
aiwaf flask --help
aiwaf fast --help
aiwaf fastapi --help

# FastAPI route-aware training using the application's runtime storage
aiwaf fast train --app app:app --log-dir aiwaf_logs --disable-ai
aiwaf fast train --app app:app --log-dir aiwaf_logs --model-path model.json

Bare CLI commands select the single installed supported framework. If multiple frameworks are installed, specify aiwaf flask ..., aiwaf fastapi ..., or aiwaf django <management-command> .... The fast alias remains supported. Django uses its management command names, such as aiwaf_list.

The Flask CSV management CLI reuses existing policy files in the current project's aiwaf_data (preferred) or .aiwaf/data before searching user and package data directories. AIWAF_DATA_DIR or aiwaf flask --data-dir /absolute/path status overrides discovery, including for a new directory. Set the same absolute AIWAF_DATA_DIR for the Flask app and CLI when running them from different working directories.

Flask, FastAPI, Django CSV mode, and the native Rust pipeline now use the same policy files: blacklist.csv, whitelist.csv, keywords.csv, geo_blocked_countries.csv, and path_exemptions.csv. For FastAPI, configure storage.backend = "csv" and storage.data_dir = "/absolute/path/aiwaf_data"; AIWAF_DATA_DIR also supplies the directory. Memory, JSON-file, database, and Django ORM modes retain their existing storage behavior.

Startup imports a legacy runtime_store.csv in the chosen directory, or the old default aiwaf_data.csv beside the project's aiwaf_data directory. Existing custom storage.file_path settings remain accepted as migration sources: /path/custom.csv maps to /path/custom/, while runtime_store.csv maps to its parent. Prefer an explicit data_dir for the application and CLI. Original legacy files remain intact, pre-import canonical files are backed up, and a completion marker prevents replay after blocks or keywords are removed. Existing canonical policy wins conflicts. Invalid legacy input stops startup with an error; repair it and restart to retry an incomplete import. Stop old workers before upgrading: subsequent writes to a legacy source are not reimported.

Keyword frequencies and non-policy runtime history are retained in runtime_metadata.csv; policy enforcement reads the shared policy files. The low-level CSVStorage key/value class remains available for explicit use, but framework CSV initialization selects PolicyCSVStorage.

FastAPI also exposes aiwaf.fast.trainer.train_from_logs(app, ...). Training refreshes learned keywords and can write a Rust JSON model artifact. Existing anomaly middleware instances require an explicit model reload or application restart to use the newly trained model.

JavaScript package

The npm package uses CommonJS and exports the Express middleware as its default function plus adapters for the other supported Node frameworks.

Express

const express = require('express');
const aiwaf = require('aiwaf');

const app = express();
app.use(express.json());

app.use(aiwaf({
  AIWAF_MIDDLEWARES: ['auto'],
  staticKeywords: ['.php', '.env', '.git'],
  WINDOW_SEC: 10,
  MAX_REQ: 20,
  FLOOD_REQ: 40,
  AIWAF_HEADER_VALIDATION: true,
  AIWAF_METHOD_POLICY_ENABLED: true,
  AIWAF_ALLOWED_METHODS: ['GET', 'POST', 'HEAD', 'OPTIONS']
}));

app.get('/', (req, res) => res.json({ protected: true }));
app.listen(3000);

Attach AIWAF after the body parser when honeypot rules inspect parsed form or JSON fields.

Node framework adapters

JavaScript path policies

app.use(aiwaf({
  AIWAF_PATH_RULES: [
    { PREFIX: '/health/', DISABLE: ['header_validation', 'rate_limit'] },
    { PREFIX: '/api/public/', RATE_LIMIT: { WINDOW: 60, MAX: 300 } }
  ]
}));

Applications can also use aiwaf.exempt, aiwaf.exemptFrom, aiwaf.only, and aiwaf.requireProtection to build route policies.

Generate an Express route manifest after routes are registered:

aiwaf.generateExpressManifest(app, '.aiwaf/paths.json');

Or generate one from a route list with the CLI:

cd js
npm run aiwaf -- manifest --framework express --routes routes.json --output .aiwaf/paths.json

JavaScript configuration

AIWAF loads aiwaf-wasm when available and falls back to the JavaScript implementation when WASM initialization or conversion is not worthwhile. Small, object-heavy operations can be faster in JavaScript because crossing the JS/WASM boundary and serializing objects has a fixed cost. WASM is most useful for sufficiently large feature batches and model scoring workloads.

The Python filesystem CSV transactions and retained training bindings remain PyO3-specific. Node now also has an explicit stateful WASM request pipeline; its transport and filesystem I/O remain in JavaScript.

Rust-owned Node request pipeline

Node 1.1.2 now requires aiwaf-wasm>=0.2.2,<0.3. From the current source checkout, build the companion package with NativeRequestPipeline support before using this adapter. Registry packages may not yet contain this new API. Publish the WASM 0.2.2 companion first; refresh the npm lockfile's registry integrity after publication, then run Node release gates and publish the Node package. The existing aiwaf(options) integration remains the legacy path; it does not switch automatically.

const express = require('express');
const aiwaf = require('aiwaf');

async function main() {
  const app = express();
  // Configure trust proxy for your actual deployment; the native adapter uses req.ip.
  app.use(express.json({ limit: '64kb' }));
  app.use(express.urlencoded({ extended: false, limit: '64kb' }));
  const firewall = await aiwaf.native({
    dataDir: 'aiwaf_native_data',
    profile: { rate_key_mode: 'ip_path', min_form_time: 1.0 },
    // Optional synchronous adapter facts; use your application's authentication.
    authenticated: req => Boolean(req.user),
    routeExists: req => req.path === '/api/ok'
  });
  app.use(firewall);
  app.get('/api/ok', (_req, res) => res.json({ ok: true }));
  app.listen(3000);
}
main().catch(error => { console.error(error); process.exitCode = 1; });

Configuration and native API checks happen once at startup. Rust runs geo, IP/keyword/structured SQL inspection, rate/flood, anomaly block state, honeypot, UUID and header checks in one begin call. A single finish call owns response observations, UUID scores, anomaly features/history/inference, bounded keyword learning, and escaped CSV row formatting. Node normalizes framework facts, serializes the request envelope, emits responses, and appends the returned rows using cached file descriptors. Logging is immediate, not queued or batched.

The dedicated lowercase profile rejects unknown keys. It does not import legacy settings, PATH_RULES/manifests, Redis, custom stores, UUID resolver callbacks or ORM state. Missing native support fails startup with no JavaScript policy fallback. Install this adapter instead of the existing chain, not alongside it. Bounded body parsers must run first; an unparsed body with a declared length/chunked encoding is rejected with 413 when inspection is enabled. Upload streams require another integration path. countryLookup(req), authenticated(req) and routeExists(req) are optional synchronous fact callbacks; country defaults to unknown and route existence to Boolean(req.route). Supply accurate facts when the firewall runs before Express resolves routes. Country headers are not implicitly trusted.

All eight stages are enabled by default. Native defaults include:

Rate counters, honeypot timestamps, UUID scores, anomaly history and loaded policy snapshots are local to each engine/worker and reset on restart. Blocks and learned keywords append to dedicated native_blocks.csv/native_keywords.csv, synced immediately, and replay at startup (persistBlocks: false disables this disk history). Policy files are not live synchronized across workers; do not share the native state/log files across worker processes. These schemas and block rules are separate from legacy reputation/CLI stores. Restart/reconstruct to apply changed policies; no backend migration or automatic legacy CSV import occurs.

native_requests.csv is written immediately; logFsync defaults to false and optionally syncs every log row. logPath overrides its location. CSV formatting and policy decisions run in Rust; Node necessarily owns actual filesystem calls. Finalization runs on response finish (or close, recorded as 499), uses a Node monotonic elapsed clock, and executes once per engine-owned context. Stop requests, wait for responses, then call firewall.close() to release writers/WASM state. Logging/persistence errors invoke onError(error) or report to stderr; an already completed response cannot be retracted. After rotating native log files, reload workers to reopen the cached writers; ordinary appends do not check file metadata or reopen descriptors per request.

Pass a fitted six-feature Rust/WASM forest as model (from aiwaf.wasm.createIsolationForest with __aiwafWasm === true). The engine copies its snapshot; firewall.setModel(model) replaces it and setModel(null) disables inference. Node's feature order remains [pathLen, kwHits, statusIdx, responseTime, burst, total404], with response time in milliseconds, distinct from Python's contract. Model scoring/behavior gates at response finalization can update block state for later requests. No model means history/learning without model blocking. firewall.diagnostics() reports native activation, actual inference count and state sizes. There are no historical request-log database reads per request.

Fastify/Hapi/Koa/Nest/Next/Adonis/Sails can select the same engine with the sole outer option AIWAF_NATIVE_PIPELINE: { profile: {...}, dataDir: '...' }. Do not mix legacy options. Fastify initializes during registration and executes at preValidation; Hapi initializes during registration, begins enforcement on onRequest (including unknown routes), then makes an additional native call at onPreHandler for parsed-body inspection and authenticated form timing. Koa/Adonis wrappers and Next API handlers expose .ready; Nest's middleware class exposes static .ready; await those promises before starting the application. Fastify/Hapi close the engine on shutdown; use the resolved native handle's close() for the other wrappers. Route metadata must be installed before enforcement; required middleware overrides decorator exemptions, while profile-level exemptions remain global. Next integration is Node API routes, not Edge/Fetch middleware.

Run npm run test:native in js/ against the built WASM package and node scripts/benchmark_js_pipeline.js for full HTTP comparisons. The benchmark subtracts bare Express server processing latency and includes synchronous response finalization/logging. Its legacy comparison uses SQLite policy lookups and JSONL logging, while native uses Rust state and CSV output; this is an integration comparison with different persistence/policy semantics, not a pure language test.

The matched Windows HTTP benchmark (Node v22.18.0, Express 4.22.1) measured:

Server samples include synchronous finalization and immediate logging. Overhead subtracts the matched bare Express baseline; figures are medians of per-run means. Legacy uses SQLite policy lookups and one immediate JSONL append; native uses Rust state and one immediate CSV append. These stores/policies have different persistence semantics, so this is an integration comparison, not a pure Rust-versus-JavaScript equivalence test. GeoIP is an unknown-country fixture, not a real database lookup. Synthetic local latency does not establish production throughput or latency.

JavaScript training and operations

cd js

AIWAF_ACCESS_LOG=/path/to/access.log npm run train
npm run aiwaf -- status
npm run aiwaf -- list blacklist
npm run aiwaf -- add blacklist 203.0.113.9 "manual block"
npm run aiwaf -- add ip-exemption 203.0.113.10 "trusted monitor"
npm run aiwaf -- geo summary
npm run aiwaf -- model info
npm run aiwaf -- export aiwaf-export.json

Use Redis or another shared cache for consistent rate limiting across multiple Node processes. Without shared state, the in-memory fallback is process-local.

Rust accelerator for Python

aiwaf-rust exposes the shared Rust core through PyO3 and ABI3 wheels for Python 3.8 and newer.

pip install aiwaf-rust

The module provides:

• validate_headers and validate_headers_with_config
• KeywordMatcher and RouteMatcher
• request-record construction and six-feature extraction
• incremental feature extraction with explicit state
• recent-behavior analysis
• IsolationForest training, scoring, prediction, retraining, and JSON state
• locked CSV storage, indexed snapshot lookups, atomic policy mutations, and synchronous cached log writers
• shared request policies, combined route plans, and exemption matching
• batched log/timestamp preparation, TrainingCSVReader, NativeTrainingBatch, and NativeTrainingBuilder

import aiwaf_rust

reason = aiwaf_rust.validate_headers({
    "HTTP_USER_AGENT": "Mozilla/5.0",
    "HTTP_ACCEPT": "text/html",
})

forest = aiwaf_rust.IsolationForest(
    n_estimators=100,
    max_samples="auto",
    contamination="auto",
    max_features=1.0,
    bootstrap=False,
    random_state=42,
    warm_start=False,
)
forest.fit([[0.1, 1.0], [0.2, 1.1], [9.0, 9.0]])
score = forest.anomaly_score([9.0, 9.0])
state = forest.to_json()
restored = aiwaf_rust.IsolationForest.from_json(state)

For a local native build, stage the two canonical repository assets and run maturin:

cp README.md rust/README.md
cp LICENSE rust/LICENSE
cp NOTICE rust/NOTICE
cp -R licenses rust/
python -m pip install maturin
cd rust
maturin develop

The staged files are ignored by Git. Release automation performs the same staging before building wheels and the source distribution.

WebAssembly package

aiwaf-wasm exposes the shared Rust core to browsers and Node-compatible bundlers.

import init, {
  IsolationForest,
  KeywordMatcher,
  RouteMatcher,
  analyze_recent_behavior,
  extract_features,
  validate_headers,
} from 'aiwaf-wasm';

await init();

const reason = validate_headers({
  'user-agent': 'Mozilla/5.0',
  accept: 'text/html'
});

const forest = new IsolationForest({
  n_estimators: 100,
  max_samples: 'auto',
  contamination: 'auto',
  random_state: 42
});
forest.fit([[0.1, 1.0], [0.2, 1.1], [9.0, 9.0]]);
const score = forest.anomaly_score([9.0, 9.0]);

The WASM API includes header, URL, and content validation; keyword and route matchers; record conversion; batched feature extraction; recent-behavior analysis; and Isolation Forest lifecycle methods. Browser use requires a bundler that understands WebAssembly assets.

Build and test it locally:

cargo test --manifest-path rust/Cargo.toml --locked -p aiwaf_core
cd rust/crates/aiwaf_wasm
wasm-pack test --node
wasm-pack build --release --target bundler
cd ../..
python scripts/patch_wasm_pkg.py
cp ../LICENSE crates/aiwaf_wasm/pkg/LICENSE
cp ../NOTICE crates/aiwaf_wasm/pkg/NOTICE
cp -R ../licenses crates/aiwaf_wasm/pkg/

Java package

AIWAF Java 1.3.3 targets Java 17, Spring Framework 7.0, and Jakarta Servlet 6.1. It is a native Java implementation and does not load the Rust library.

Core engine

Use the two-stage API when the integration can observe the handler response. Deterministic controls run before the application; anomaly scoring then receives the actual status and elapsed time.

import com.aiwaf.core.AiwafConfig;
import com.aiwaf.core.AiwafDecision;
import com.aiwaf.core.AiwafEngine;
import com.aiwaf.core.AiwafRequest;

import java.util.Map;
import java.util.Set;

AiwafConfig config = new AiwafConfig();
config.rateLimitEnabled = true;
config.rateLimitMax = 20;
config.rateLimitWindowSeconds = 10;

AiwafEngine engine = new AiwafEngine(config);
AiwafRequest request = new AiwafRequest(
    "GET",
    "/api/profile",
    "203.0.113.10",
    "US",
    Map.of("User-Agent", "Mozilla/5.0", "Accept", "application/json"),
    Map.of(),
    System.currentTimeMillis(),
    Set.of()
);

long started = System.currentTimeMillis();
AiwafDecision decision = engine.evaluateBeforeResponse(request);
if (decision.allowed()) {
    decision = engine.evaluateAfterResponse(
        request,
        200,
        System.currentTimeMillis() - started
    );
}

evaluate(request) remains available for request-only integrations and uses provisional response values for AI features.

Spring MVC

Spring Boot applications are protected automatically. Adding aiwaf-java creates AiwafConfig, AiwafEngine, and the ordered AiwafFilter; discovers live MVC routes, route annotations, and JPA UUID fields; and applies request controls before the controller plus post-response scoring with the real status and timing. Set aiwaf.enabled=false to disable auto-configuration.

aiwaf.rate-limit.max=60
aiwaf.rate-limit.window-seconds=60
aiwaf.geo.enabled=true
aiwaf.geo.blocked-countries=CN,RU
aiwaf.path-manifest.path=.aiwaf/paths.json
# Use one namespace and shared enforcement state across application instances.
aiwaf.storage.backend=redis
aiwaf.storage.redis-url=redis://localhost:6379/0
aiwaf.storage.key-prefix=my-service
# Redis is fail-closed by default. Every wait is bounded.
aiwaf.storage.redis-failure-mode=fail_closed
aiwaf.storage.redis-connection-timeout-millis=1000
aiwaf.storage.redis-socket-timeout-millis=2000
aiwaf.storage.redis-pool-max-total=32
aiwaf.storage.redis-pool-max-idle=16
aiwaf.storage.redis-pool-min-idle=0
aiwaf.storage.redis-pool-max-wait-millis=250

Set aiwaf.config-file=/etc/aiwaf/config.json (or AIWAF_CONFIG_FILE) to load the same nested JSON configuration used by the Python runtime. Environment variables and Spring properties override file values. AiwafConfigFileCore also exposes load, save, deep-merge, and validation APIs for non-Spring applications.

Define an AiwafConfig, AiwafEngine, or AiwafFilter bean only when the application needs a custom replacement. For plain Spring MVC without Boot, register the filter directly; passing the application context enables the same live route discovery:

@Bean
AiwafFilter aiwafFilter(AiwafEngine engine, ApplicationContext context) {
    return new AiwafFilter(engine, context);
}

The live mapping integration honors @AiwafExempt, @AiwafExemptFrom, @AiwafOnly, and @AiwafRequireProtection without a controller list. Do not register AiwafInterceptor and AiwafFilter as independent enforcement layers for the same request.

Committed, streaming, and asynchronous responses are recorded but are not replaced after bytes have been sent.

UUID protection uses Python-compatible weighted signals: malformed input, valid UUID requests that resolve to 404, and successful requests that decay the score. UUID-valued query parameters and {uuid} route variables are supported. SpringUuidModelLookup optionally discovers UUID primary keys and unique UUID properties through the JPA metamodel; omit it when the application does not use JPA.

Generate a route manifest from the live Spring application rather than supplying controller methods manually:

java -cp "your-app.jar:aiwaf-java-1.3.3.jar" \
  com.aiwaf.cli.AiwafConsole init \
  --app com.example.Application \
  --output .aiwaf/paths.json

The command starts the application, reads every registered RequestMappingHandlerMapping, writes the shared schema_version/framework/context_hash/routes document, and then closes the temporary application context. At runtime, AiwafEngine loads this file automatically and appends its disabled middleware and rate-limit overrides after explicit pathRules, so explicit equal-prefix rules retain precedence. Set pathManifestEnabled=false or aiwaf.path-manifest.enabled=false to opt out.

Jakarta Servlet

AiwafConfig config = new AiwafConfig();
AiwafEngine engine = new AiwafEngine(config);

servletContext
    .addFilter("aiwaf", new AiwafServletFilter(engine))
    .addMappingForUrlPatterns(null, false, "/*");

The generic servlet filter uses request-time evaluation. Spring applications should prefer AiwafFilter for handler metadata and post-response scoring.

Java configuration and models

GeoIP uses the bundled MMDB through the native Java reader; it does not require the external mmdblookup executable. A valid X-Country value is accepted only from a peer in trustedProxyCidrs; otherwise AIWAF resolves the client address itself. Default blacklist calls use Python-compatible reputation scoring and temporary escalation (15 minutes, 1 hour, then 24 hours). Use BlacklistManager.blockPermanent(ip, reason) for an explicit permanent administrative block.

storageBackend="redis" makes rate-limit buckets, honeypot timing, weighted UUID scores, recent-request anomaly state, blacklist data, exemptions, geo policy, and learned keywords visible to every application instance using the same key prefix. Redis enforcement updates use atomic server-side scripts. Memory, file, CSV, and SQLite engines keep request state isolated per AiwafEngine; constructing another engine no longer replaces an existing engine's stores. The static RuntimeStorage and BlacklistManager methods remain compatibility facades for command-line and administrative code.

The Java 1.3 Redis backend supports standalone Redis only. Sentinel and Cluster configurations are rejected during validation instead of silently operating with incomplete semantics. fail_closed is the default: an unavailable Redis instance prevents startup or raises a request-path storage failure, which results in the application denying availability rather than bypassing protection. fail_open permits startup and returns non-blocking enforcement fallbacks while Redis is unavailable; failed mutations return false at the StorageBackend boundary. Connection, socket, pool size, and pool wait limits are bounded and configurable. RedisStorage.diagnostics() reports operation/failure counts, latency, and live pool utilization.

Redis keys include a state-schema version, and each base key prefix has an atomic compatibility marker. Releases using the same schema can share state during a rolling upgrade. A runtime with an incompatible schema refuses the namespace—even in fail_open mode. Use a unique key prefix per application and environment; deploy an incompatible schema with a new prefix, migrate deliberately, then retire the old namespace. Serialized persistent values remain JSON within a schema version.

New Java models use the Python-aligned six-feature schema and are written as safe JSON containing the Python aiwaf_rust.IsolationForest state. Python can restore Java-generated forest state, and Java can load Python Rust-model artifacts. Existing signed Java binary artifacts, including nine-feature models, remain readable through the restricted legacy loader. Scikit-learn object artifacts are intentionally unsupported.

Java operational commands mirror the common Python workflow:

aiwaf-cli status
aiwaf-cli diagnose --model model.json
aiwaf-cli blocked
aiwaf-cli unblock 203.0.113.10
aiwaf-cli clear
aiwaf-cli train --events events.json --out model.json --backend java
aiwaf-cli replay --cases replay.json
aiwaf-cli model status --path model.json
aiwaf-cli logs --dir aiwaf_logs

Build Java from the repository root:

cd java
mvn test
mvn -Dgpg.skip=true package

The build produces the main, source, and Javadoc JARs under java/target/. The main JAR embeds the root license as META-INF/LICENSE. FastR training is optional and falls back to the Java trainer when R or jsonlite is unavailable.

Shared training schema

New training paths use these six features in this order:

1. path_len
2. kw_hits
3. resp_time
4. status_idx
5. burst_count
6. total_404

resp_time is expressed in seconds in feature vectors. Artifact summary fields explicitly named with _ms remain milliseconds.

The training lifecycle is:

1. Parse configured access logs or middleware-generated request logs.
2. Normalize request paths, status, timing, and per-IP state.
3. Extract the six features and suspicious route segments.
4. Train or update the Isolation Forest after configured volume thresholds are met.
5. Refresh learned keywords while excluding known legitimate and exempt routes.
6. Persist portable JSON forest state and runtime metadata; legacy language-specific artifacts remain read-only compatibility inputs.

Do not train directly on unreviewed sensitive request bodies or authentication headers. Treat models, logs, exported state, and MMDB files as deployment artifacts with controlled access.

Deployment guidance

• Configure the application to trust forwarding headers only from known proxy CIDRs.
• Configure the outermost proxy to overwrite client-supplied forwarding headers.
• Use Redis, a database, or another shared backend when multiple workers must share rate-limit, blacklist, or exemption state.
• Keep health checks, static assets, and trusted webhook routes explicitly scoped instead of disabling protection globally.
• Start anomaly detection in observe-only or conservative mode and tune it with representative normal traffic.
• Rotate logs and exclude secrets, authorization headers, cookies, and personal data.
• Sign model/configuration artifacts where the runtime supports HMAC verification.
• Run AIWAF alongside normal secure-development controls and an edge/reverse-proxy layer.

Development and tests

Whole monorepo

Create a Python environment, install all adapters, then install Node dependencies:

python -m pip install -e ".[django,flask,fastapi,rust]"
python -m pip install pytest pytest-cov coverage httpx
npm ci --prefix js
python aiwaf_test.py --coverage

Focused suites:

python aiwaf_test.py --python-only
python aiwaf_test.py --rust-only
python aiwaf_test.py --js-only
python aiwaf_test.py --wasm-only

Java is tested separately:

cd java
mvn test

Direct package commands

# Python
pytest

# Node.js
npm ci --prefix js
npm test --prefix js

# Rust core
cargo test --manifest-path rust/Cargo.toml --locked -p aiwaf_core

# Rust Python API, after installing the local wheel
pytest -q rust/tests/test_python_api.py

# Java
mvn -f java/pom.xml test

Benchmark

The in-process Flask benchmark compares a baseline app, auto middleware selection, and the full middleware chain:

python -m benchmarks.benchmark_auto_middleware
python -m benchmarks.benchmark_auto_middleware --path-rules 250
python -m benchmarks.benchmark_auto_middleware --requests 200 --rounds 3 --warmup 50

Use the same machine, environment, request count, and background load when comparing results. These measurements exclude network and production-server overhead.

The latest native optimization measurements used three alternating runs. Timestamp and feature/model tests used 10,000 synthetic records; the model test used a 20-tree forest and compared the previous Rust-feature/Python-matrix path with retained native features.

Native ISO parsing was slower on both platforms, so it remains optional. An offline Windows Flask test-client workload with IP/keyword enforcement and CSV logging measured 5.95 ms/request native versus 9.18 ms/request Python (1.54×), using 100 initial blacklist rows and 20 requests per trial. These measurements exclude networking, production-server overhead, and concurrent request load; they do not establish a complete training or deployed-site speedup.

Reproduce the focused measurements and concurrency smoke check locally:

python scripts/benchmark_native_training_boundary.py
python scripts/benchmark_native_csv_training.py
python scripts/benchmark_native_csv_workloads.py
python scripts/benchmark_csv_request_reuse.py
python scripts/profile_csv_logging.py
python scripts/check_csv_release_concurrency.py

Validation of the development changes passed 282 Django tests, 134 FastAPI tests (one skipped), and 247 Flask/core optimization tests in separate processes. Coverage includes timestamp fallbacks, gzip/quoted CSV, seeded model parity, chunk equivalence, concurrent mutations, no-op writes, and log rotation/reopening. An installed-wheel Linux smoke check with four processes retained all 160 access rows and 80 blacklist entries. Local wheels were checked on Windows Python 3.13 and WSL Python 3.10; the locally built Linux wheel requires glibc 2.34 or newer. The complete supported platform/version matrix and production load validation remain release checks.

Release automation

Release versions and tags must match the relevant manifest.

Python and Rust publish to PyPI with trusted publishing. The npm workflows use npm trusted publishing and stage packages for approval. Configure the trusted publisher with organization aiwaf-project, repository aiwaf, the exact workflow filename, and the environment used by that workflow.

Java publishing uses the protected maven-publish environment plus GPG_PRIVATE_KEY, GPG_PASSPHRASE, CENTRAL_TOKEN_USERNAME, and CENTRAL_TOKEN_PASSWORD. The workflow verifies the tag, runs tests, signs the POM and all three JARs, uploads the deployment bundle, and waits for Maven Central publication.

Registry releases are immutable. Increment the relevant manifest version before publishing another release.

Security and limitations

• Deterministic controls can produce false positives when legitimate routes resemble common probes; use narrow route exemptions and regression tests.
• Language-native Isolation Forest implementations can produce different scores for identical feature rows.
• WASM object conversion has overhead and is not automatically faster than JavaScript for small calls.
• In-memory rate limits and runtime stores are local to one process.
• GeoIP data can be missing, stale, or imprecise and should not be the sole authorization control.
• Post-response blocking cannot replace data already committed or streamed to a client.
• No WAF can compensate for broken authorization or unsafe application logic.

Report vulnerabilities privately to the maintainers rather than opening a public exploit issue.

Contributing

See CONTRIBUTING.md for contributor setup and expectations, CHANGELOG.md for release history, and CODE_OF_CONDUCT.md for community standards.

License

Original AIWAF project code in this checkout uses the unmodified Elastic License 2.0, with separately licensed material identified in NOTICE. AIWAF is the business offering these terms. ELv2 permits commercial use, modification, redistribution, and consulting, subject to its conditions. It restricts providing substantial AIWAF functionality as a hosted or managed service, bypassing license-key functionality, and removing required notices. ELv2-covered code is source available.

This migration applies to this checkout and future releases expressly distributed under ELv2. Previously distributed MIT copies retain their permissions; version numbers do not retroactively change their license. Do not republish an existing version with new licensing terms. Independently versioned packages record their license in their release artifacts.

Retained MIT contributions keep their rights and notices in licenses/MIT.txt. The bundled IPinfo Lite database is provided by IPinfo under CC BY-SA 4.0; ELv2 restrictions do not apply to that data. Package metadata uses an AND expression where separately licensed components are included; this does not offer a choice to license the entire package under MIT. Dependencies retain their respective terms.

Redistributions must include the applicable LICENSE, NOTICE, and licenses/ files. See NOTICE for historical rights, contributor notices, and attribution.