RSigma

The RSigma project is a complete Rust toolkit for the Sigma detection standard, including a parser, evaluation engine, rule conversion, streaming runtime, linter, CLI, and LSP.

RSigma parses Sigma YAML rules into a strongly-typed AST, compiles them into optimized matchers, and evaluates them against log events in real time. It handles stateful correlation logic in-process with memory-efficient compressed event storage. Or as Zack Allen put it in DEW #149, "RSigma is essentially a SIEM."

You can send events in many formats, including JSON, syslog (RFC 3164/5424), logfmt, CEF, EVTX (Windows Event Log), plain text, and OTLP (OpenTelemetry Protocol), with auto-detection by default. pySigma-compatible processing pipelines handle field mapping and backend configuration. OTLP support lets any OpenTelemetry-compatible agent (Grafana Alloy, Vector, Fluent Bit, OTel Collector) forward logs to RSigma via HTTP or gRPC for detection.

For rule quality and editor integration, a built-in linter validates rules against 66 checks derived from the Sigma v2.1.0 specification, and an LSP server provides real-time diagnostics, completions, hover documentation, and quick-fix code actions in any editor.

Supported Features

Parse Sigma YAML into a strongly-typed AST with support for detection, correlation, and filter rules
Compile and evaluate rules against JSON events in real time with stateless detection and stateful correlation (sliding windows, group-by, chaining, suppression)
Accept JSON, syslog (RFC 3164/5424), logfmt, CEF, EVTX (Windows Event Log), plain text, and OTLP logs with format auto-detection
pySigma-compatible processing pipelines for field mapping, transformations, conditions, and finalizers
Dynamic pipelines: populate any pipeline value from external sources (HTTP, files, commands, NATS) with template expansion, auto-refresh, and data extraction via jq, JSONPath, or CEL
Post-evaluation enrichment: inject contextual data (asset info, IP reputation, identity, GeoIP, runbook URLs, ...) into detection and correlation results via four primitives (template, lookup, http, command) with kind-aware template namespaces, response cache, scope filtering, and hot-reload
Convert rules into backend-native query strings via a pluggable backend trait (PostgreSQL/TimescaleDB SQL, LynxDB)
Optional eval prefilters for large rule sets: bloom filter for substring matchers (--bloom-prefilter) and cross-rule Aho-Corasick index for whole-rule pruning (--cross-rule-ac, requires daachorse-index feature)
Run as a streaming detection daemon with hot-reload, Prometheus metrics, and HTTP/NATS/OTLP input
In-process TLS termination for the daemon API listener (HTTP REST, /metrics, OTLP/HTTP, OTLP/gRPC) with optional mutual TLS, aws-lc-rs crypto, and cross-platform certificate hot-reload
NATS JetStream support with authentication (credentials, mTLS), replay, consumer groups, and dead-letter queues
OTLP support for any OpenTelemetry-compatible agent (Grafana Alloy, Vector, Fluent Bit, OTel Collector) via HTTP or gRPC
Built-in linter with 66 checks, four severity levels, a full suppression system, and auto-fix (--fix) for 13 safe rules
LSP server with real-time diagnostics, completions, hover documentation, document symbols, and quick-fix code actions
Multi-arch Docker images (linux/amd64, linux/arm64) with cosign signatures, SBOM, and SLSA Build L3 provenance
Cross-platform binaries for Linux, macOS, and Windows on amd64 and arm64

Crates

Crate	Description
`rsigma-parser`	Parse Sigma YAML into a strongly-typed AST
`rsigma-eval`	Compile and evaluate rules against JSON events
`rsigma-convert`	Transform rules into backend-native query strings
`rsigma-runtime`	Streaming runtime with input adapters, log processor, and hot-reload
`rsigma`	CLI for parsing, validating, linting, evaluating, converting rules, field catalog, and running a detection daemon
`rsigma-lsp`	Language Server Protocol (LSP) server for IDE support

Tip

To learn more about RSigma, read these articles:

Note

RSigma has been featured in:

Detection Engineering Weekly #149 (March 2026) "Building a tool like RSigma is challenging because the Sigma specification has evolved into a robust domain-specific language over the years."
tl;dr sec #320 (March 2026) "Accurately evaluating the full spectrum of what Sigma rules can express is quite complex, it's pretty neat to read about how RSigma handles all of these conditional expressions, correlating across rules, etc."
The Deep Purple Sec by BlackNoise - March 2026 (April 2026) "Defensive teams can pipe logs through CLI commands, apply field-mapping pipelines, and chain correlations for multi-stage attack detection."
Detection Engineering Weekly #154 (April 2026) "RSigma is not a SIEM, but it's an impressive feat to build a self-contained Rust binary that operates much like one. For teams doing pre-SIEM rule validation or forensics, it's a solid plug-and-play option."
Detection Engineering Weekly #157 (May 2026) "Instead of hardcoding IOC values in rule YAML, you declare external sources in the pipeline config, and RSigma fetches and injects them at evaluation time. This works very similarly to how I've seen SIEMs implement threat intelligence pipelines, but since it's RSigma, it's self-contained within its ecosystem."

Installation

# Build all crates
cargo build --release --all-features --workspace

# Install the CLI
cargo install --locked rsigma

# Install the LSP server
cargo install --locked --path crates/rsigma-lsp

Docker

Multi-arch images (linux/amd64, linux/arm64) are published to GHCR on every release.

docker pull ghcr.io/timescale/rsigma:latest
docker run --rm ghcr.io/timescale/rsigma:latest --help

Run with full runtime hardening:

docker run --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges:true \
  -v /path/to/rules:/rules:ro \
  ghcr.io/timescale/rsigma:latest rule validate /rules/

Verify the image signature:

cosign verify \
  --certificate-identity-regexp 'github.com/timescale/rsigma' \
  --certificate-oidc-issuer https://token.actions.githubusercontent.com \
  ghcr.io/timescale/rsigma:latest

Quick Start

# Evaluate a single event against Sigma rules
rsigma engine eval -r rules/ -e '{"CommandLine": "cmd /c whoami"}'

# Stream NDJSON from stdin
cat events.ndjson | rsigma engine eval -r rules/

# Run as a daemon with hot-reload and Prometheus metrics
rsigma engine daemon -r rules/ -p ecs.yml --api-addr 0.0.0.0:9090

# Accept events via HTTP POST
rsigma engine daemon -r rules/ --input http

# Convert rules to PostgreSQL SQL
rsigma backend convert rules/ -t postgres

See the CLI README for complete documentation of all subcommands and flags.

Daemon Input Modes

The daemon accepts events from multiple sources. The --input flag selects the primary source, and OTLP is always available as an additional ingestion path when the daemon-otlp feature is enabled.

# stdin (default): pipe events from any source
hel run | rsigma engine daemon -r rules/ -p ecs.yml

# HTTP: POST NDJSON events to /api/v1/events
rsigma engine daemon -r rules/ --input http
curl -X POST http://localhost:9090/api/v1/events -d '{"CommandLine":"whoami"}'

# NATS JetStream (requires daemon-nats feature)
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> --output nats://localhost:4222/detections

# OTLP (requires daemon-otlp feature): always active alongside any --input mode
# Agents (Grafana Alloy, Vector, Fluent Bit, OTel Collector) send logs to /v1/logs (HTTP) or gRPC
rsigma engine daemon -r rules/ --input http
curl -X POST http://localhost:9090/v1/logs -H 'Content-Type: application/json' -d '{"resourceLogs":[...]}'

NATS Streaming

Production-grade NATS JetStream support with authentication, at-least-once delivery, replay, and horizontal scaling via consumer groups.

# Credentials file authentication
rsigma engine daemon -r rules/ --input nats://nats.example.com:4222/events.> --nats-creds /etc/rsigma/nats.creds

# Mutual TLS
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> \
  --nats-tls-cert client.pem --nats-tls-key client-key.pem --nats-require-tls

# Replay from a point in time
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> --replay-from-time 2026-04-30T00:00:00Z

# Replay with automatic state restore (forward catch-up)
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> --replay-from-sequence 1001 --state-db state.db

# Consumer groups for horizontal scaling
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> --consumer-group detection-workers

# Dead-letter queue for events that fail processing
rsigma engine daemon -r rules/ --input nats://localhost:4222/events.> --dlq file:///var/log/rsigma-dlq.ndjson

TLS Termination

Optional in-process TLS termination for the daemon's API listener (HTTP REST, /metrics, OTLP/HTTP, OTLP/gRPC), all on one socket. Requires the daemon-tls build feature. ALPN advertises both h2 and http/1.1 so legacy REST clients and modern gRPC clients share the listener. The daemon refuses to start on a non-loopback --api-addr without TLS or --allow-plaintext; loopback always allows plaintext for local development.

# HTTPS for every protocol on --api-addr
rsigma engine daemon -r rules/ --input http --api-addr 0.0.0.0:9090 \
  --tls-cert /etc/rsigma/tls/server.crt \
  --tls-key  /etc/rsigma/tls/server.key

# Mutual TLS: every agent must present a CA-signed client cert
rsigma engine daemon -r rules/ --input http --api-addr 0.0.0.0:9090 \
  --tls-cert /etc/rsigma/tls/server.crt \
  --tls-key  /etc/rsigma/tls/server.key \
  --tls-client-ca /etc/rsigma/tls/clients-ca.crt

Certificate hot-reload is cross-platform: POST /api/v1/reload, SIGHUP (Unix), or a YAML file change picked up by the file watcher all trigger the central reload task, which re-reads the cert/key from disk and atomically swaps the active rustls::ServerConfig via Arc<ArcSwap<…>> without dropping inflight connections. Two extra Prometheus gauges (rsigma_tls_certificate_expiry_seconds and rsigma_tls_active_connections) make the rotation observable.

Input Formats and Pipelines

Events are parsed with auto-detection by default (JSON, syslog, plain text). Feature-gated formats: logfmt, cef, evtx. Processing pipelines handle field mapping between source schemas and Sigma field names.

# With a processing pipeline for field mapping
rsigma engine eval -r rules/ -p pipelines/ecs.yml -e '{"process.command_line": "whoami"}'

# Explicit syslog with timezone offset
tail -f /var/log/syslog | rsigma engine eval -r rules/ --input-format syslog --syslog-tz +0530

# logfmt (requires logfmt feature)
rsigma engine eval -r rules/ --input-format logfmt < app.log

# CEF / ArcSight (requires cef feature)
rsigma engine eval -r rules/ --input-format cef < arcsight.log

# EVTX / Windows Event Log (requires evtx feature)
rsigma engine eval -r rules/ -e @security.evtx

Dynamic Pipelines

Standard Sigma pipelines are static: every value is hardcoded in YAML. RSigma extends this with dynamic pipelines where external data sources feed into any part of a pipeline via ${source.*} template references. This means field mappings, condition values, and even entire transformation blocks can be populated from live APIs, configuration files, commands, or NATS subjects.

# pipeline.yml with dynamic sources
name: dynamic_example
sources:
  - id: threat_intel
    type: http
    url: https://intel.example.com/v1/iocs
    format: json
    extract: ".indicators[].ip"
    refresh: 300s
    timeout: 10s
    on_error: use_cached

  - id: field_map
    type: file
    path: /etc/rsigma/fields.json
    format: json
    refresh: watch

transformations:
  - id: map_fields
    type: field_name_mapping
    mapping: ${source.field_map}

  - id: block_known_bad
    type: add_condition
    conditions:
      - field: DestinationIp
        value: ${source.threat_intel}

# Test source resolution offline
rsigma pipeline resolve -p pipeline.yml --pretty

# Run the daemon with a dynamic pipeline (pipeline-embedded sources, deprecated)
rsigma engine daemon -r rules/ -p pipeline.yml

# Recommended: declare sources in a standalone file and load with --source
rsigma engine daemon -r rules/ -p pipeline.yml --source sources.yml

Sources support four types (file, HTTP, command, NATS), multiple data formats (JSON, YAML, lines, CSV), three extraction languages (jq, JSONPath, CEL), configurable refresh policies, error handling with caching, and include directives for injecting entire transformation blocks. The recommended approach is to declare sources in standalone files loaded with --source, decoupling them from pipeline YAML. Pipeline-embedded sources: blocks are deprecated; use rsigma rule migrate-sources to migrate. See the CLI README for the full reference and the runtime README for the library API.

Rule Conversion

Convert Sigma rules into backend-native queries for historical threat hunting.

# PostgreSQL SQL
rsigma backend convert rules/ -t postgres

# PostgreSQL with OCSF field mapping
rsigma backend convert rules/ -t postgres -p pipelines/ocsf_postgres.yml

# PostgreSQL views, TimescaleDB continuous aggregates, or sliding window correlation
rsigma backend convert rules/ -t postgres -f view
rsigma backend convert rules/ -t postgres -f continuous_aggregate
rsigma backend convert rules/ -t postgres -f sliding_window

# JSONB mode: access fields inside a JSONB column
rsigma backend convert rules/ -t postgres -O table=okta_events -O json_field=data -O timestamp_field=time

# LynxDB search queries
rsigma backend convert rules/ -t lynxdb

# List all fields referenced by a ruleset
rsigma rule fields -r rules/

# Show fields after pipeline mapping
rsigma rule fields -r rules/ -p ecs.yml --json

# List available backends and formats
rsigma backend targets
rsigma backend formats postgres

Library Usage

Or use the library directly:

use rsigma_parser::parse_sigma_yaml;
use rsigma_eval::Engine;
use rsigma_eval::event::JsonEvent;
use serde_json::json;

let yaml = r#"
title: Detect Whoami
logsource:
    product: windows
    category: process_creation
detection:
    selection:
        CommandLine|contains: 'whoami'
    condition: selection
level: medium
"#;

let collection = parse_sigma_yaml(yaml).unwrap();
let mut engine = Engine::new();
engine.add_collection(&collection).unwrap();

let event = JsonEvent::borrow(&json!({"CommandLine": "cmd /c whoami"}));
let matches = engine.evaluate(&event);
assert_eq!(matches[0].rule_title, "Detect Whoami");

Streaming Runtime

rsigma-runtime provides a reusable pipeline for streaming log detection. It handles input parsing (JSON, syslog, logfmt, CEF, plain text, auto-detect), batch evaluation with parallel detection + sequential correlation, atomic hot-reload via ArcSwap, and pluggable metrics.

use std::sync::Arc;
use rsigma_eval::CorrelationConfig;
use rsigma_runtime::{InputFormat, LogProcessor, NoopMetrics, RuntimeEngine};

// Load rules
let mut engine = RuntimeEngine::new(
    "rules/".into(),
    vec![],
    CorrelationConfig::default(),
    false,
);
engine.load_rules().unwrap();

let processor = LogProcessor::new(engine, Arc::new(NoopMetrics));

// Process a batch of raw log lines (any format)
let batch = vec![
    r#"{"CommandLine": "cmd /c whoami", "EventID": 1}"#.to_string(),
];
let results = processor.process_batch_with_format(
    &batch,
    &InputFormat::Json,
    None,
);

for result in &results {
    for det in &result.detections {
        println!("Detection: {}", det.rule_title);
    }
}

Input formats are selected via --input-format on the CLI or InputFormat in the library. Auto-detect (the default) tries JSON, syslog, and plain text. Feature-gated formats: logfmt, cef, evtx. EVTX files (.evtx) are binary and auto-detected by file extension when using the @path syntax.

See examples/jsonl_stdin.rs and examples/tail_syslog.rs for complete working examples.

Observability

The daemon emits Prometheus metrics on /metrics and structured JSON logs to stderr via tracing. Verbosity is controlled with RUST_LOG (default info). Useful filter targets:

Filter	Surfaces
`RUST_LOG=info,tower_http=debug`	HTTP API access logs (method, URI, status, latency) for every REST request
`RUST_LOG=info,rsigma=debug`	batch processing spans, DLQ routing, OTLP per-request fields, snapshot save duration
`RUST_LOG=info,rsigma_runtime::sources=debug`	dynamic source resolution and refresh scheduler
`RUST_LOG=info,rsigma_eval=debug`	correlation engine internals (chain depth, hard-cap eviction at `warn`)

tracing spans installed on hot paths (batch processing, source resolution, OTLP ingest, rule loading) double as profiling hooks consumable by tokio-console or tracing-timing without code changes. Non-daemon subcommands default to human-readable output; pass --log-format json (or text) to additionally install a stderr subscriber for CI/log aggregation use cases.

Architecture

Everything starts with a Sigma rule in YAML format:

Parsing: yaml_serde deserializes the YAML into a raw value, then rsigma-parser turns it into a strongly-typed AST. A PEG grammar (sigma.pest) handles the document structure while a Pratt parser (condition.rs) handles condition expressions. Supporting modules define value types (value.rs: SigmaStr, wildcards, timespans) and AST nodes (ast.rs: modifiers, enums). The result is a SigmaRule, CorrelationRule, FilterRule, or SigmaCollection.

From there, the AST can go in three directions depending on what you need:

Evaluation: rsigma-eval compiles rules into optimized matchers (compiler.rs), runs stateless detection through Engine, and tracks stateful correlation (correlation.rs: sliding windows, group-by, chaining, suppression) across events. Processing pipelines handle field mapping, transformations, conditions, and finalizers before compilation. Dynamic pipelines extend this with ${source.*} template references that are resolved at runtime from external data sources. Events are accessed through a trait with implementations for JSON, key-value, and plain text.
Conversion: rsigma-convert transforms rules into backend-native query strings through a pluggable Backend trait. A condition walker traverses the AST and delegates to the backend for each node. TextQueryConfig exposes ~90 configuration fields for text-based backends. Concrete implementations include PostgreSQL/TimescaleDB (SQL for historical threat hunting) and LynxDB (SPL2-compatible search queries for log analytics).
Editor support: rsigma-lsp provides an LSP server over stdio (via tower-lsp) with real-time diagnostics (lint + parse + compile errors), completions, hover documentation, document symbols, and code actions. Works with VSCode, Neovim, Helix, Zed, and any LSP-capable editor.

When running as a streaming detection engine, rsigma-eval feeds into rsigma-runtime:

Input: Format adapters parse raw log lines (JSON, syslog, logfmt*, CEF*, plain text, with auto-detection) into EventInputDecoded. EVTX* files are parsed directly from binary via EvtxFileReader. Sources include stdin, HTTP POST, NATS JetStream, and OTLP* (HTTP protobuf/JSON and gRPC).
Dynamic sources: SourceResolver fetches data from files, commands, HTTP APIs, and NATS subjects. Resolved values are injected into pipelines via TemplateExpander. A SourceCache (in-memory + optional SQLite) provides fallback data. RefreshScheduler manages auto-refresh (interval, file watch, NATS push, on-demand). Extraction supports jq, JSONPath, and CEL. DaemonSourceRegistry unifies sources from external files (--source) and pipeline-embedded declarations with collision-error semantics.
Processing: LogProcessor runs batch evaluation with parallel detection and sequential correlation. RuntimeEngine wraps Engine and CorrelationEngine with rule loading and ArcSwap hot-reload.
Output: Sinks write evaluation results to stdout, files, or NATS as one flat JSON object per result. Multiple sinks can run in fan-out. The output type is EvaluationResult (a composition of RuleHeader + ResultBody::Detection|Correlation), carrying rule title, id, level, tags, matched selections, field matches, aggregated values, and optionally the triggering events.

Feature-gated items are marked with * in the diagram.

Architecture diagram

                    ┌──────────────────┐
   YAML input ───>  │   yaml_serde     │──> Raw YAML Value
                    └──────────────────┘
                             │
                             ▼
                    ┌──────────────────┐
                    │   parser.rs      │──> Typed AST
                    │  (YAML → AST)    │   (SigmaRule, CorrelationRule,
                    └──────────────────┘    FilterRule, SigmaCollection)
                             │
            ┌────────────────┼──────────────┐
            ▼                ▼              ▼
     ┌────────────┐  ┌────────────┐  ┌────────────┐
     │ sigma.pest │  │  value.rs  │  │   ast.rs   │
     │  (PEG      │  │ (SigmaStr, │  │ (AST types │
     │  grammar)  │  │  wildcards,│  │  modifiers,│
     │     +      │  │  timespan) │  │  enums)    │
     │condition.rs│  └────────────┘  └────────────┘
     │  (Pratt    │
     │  parser)   │
     └────────────┘
           │
     ┌─────┴───────────────────────────────────────────────────────────┐
     │                                   │                             │
     ▼                                   ▼                             ▼
    ┌─────────────────────────┐   ┌─────────────────────┐   ┌────────────────────┐
    │      rsigma-eval        │   │   rsigma-convert    │   │    rsigma-lsp      │
    │                         │   │                     │   │                    │
    │  Event trait ──>        │   │  Backend trait ──>  │   │  LSP server over   │
    │    JsonEvent, KvEvent,  │   │    pluggable query  │   │  stdio (tower-lsp) │
    │    PlainEvent           │   │    generation       │   │                    │
    │                         │   │                     │   │  • diagnostics     │
    │  pipeline/ ──>          │   │  TextQueryConfig    │   │    (lint + parse   │
    │    Pipeline, conditions,│   │    ──> ~90 config   │   │     + compile)     │
    │    transformations,     │   │    fields for text  │   │  • completions     │
    │    state, finalizers,   │   │    query backends   │   │  • hover           │
    │    builtin pipelines    │   │                     │   │  • document        │
    │    (ecs_windows,        │   │  Condition walker,  │   │    symbols         │
    │     sysmon),            │   │    deferred exprs,  │   │                    │
    │    ${source.*} expand   │   │    conversion state │   │  Editors:          │
    │                         │   │                     │   │  VSCode, Neovim,   │
    │  compiler/ ──>          │   │  backends/ ──>      │   │  Helix, Zed, ...   │
    │    CompiledRule         │   │    TextQueryTest,   │   └────────────────────┘
    │    + matcher optimizer  │   │    PostgreSQL/      │
    │      (Aho-Corasick,     │   │    TimescaleDB,     │
    │       RegexSet, case-   │   │    LynxDB           │
    │       insens. group)    │   └─────────────────────┘
    │                         │
    │  engine/ ──>            │
    │    Engine (stateless)   │
    │    + RuleIndex          │
    │      (exact-value prune)│
    │    + bloom prefilter*   │
    │    + cross-rule AC**    │
    │      (daachorse)        │
    │                         │
    │  correlation/ ──>       │
    │    sliding windows,     │
    │    group-by, chaining,  │
    │    suppression, events  │
    │                         │
    │  rsigma.* custom        │
    │    attributes           │
    └─────────────────────────┘
              │
              ▼
    ┌──────────────────────────────────────────┐
    │            rsigma-runtime                │
    │                                          │
    │  input/ ──> format adapters:             │
    │    JSON, syslog, logfmt*, CEF*,          │
    │    EVTX*, plain text, auto-detect        │
    │    ↓ raw line → EventInputDecoded        │
    │                                          │
    │  sources/ ──> dynamic pipelines:         │
    │    SourceResolver (HTTP, command,        │
    │      file, NATS subjects)                │
    │    TemplateExpander (${source.*})        │
    │    SourceCache (in-mem + SQLite TTL)     │
    │    RefreshScheduler (interval, watch,    │
    │      NATS push, SIGHUP, control msg)     │
    │    extract: jq, JSONPath, CEL            │
    │    include directives                    │
    │    └──> feeds resolved values into the   │
    │         eval pipeline at load/refresh    │
    │                                          │
    │  LogProcessor ──> batch evaluation       │
    │    ArcSwap hot-reload                    │
    │      (rules + pipelines),                │
    │    MetricsHook,                          │
    │    EventFilter (JSON payload extraction) │
    │                                          │
    │  RuntimeEngine ──> wraps Engine +        │
    │    CorrelationEngine with rule loading   │
    │                                          │
    │  io/ ──> EventSource (stdin, HTTP, NATS) │
    │          OTLP* (HTTP + gRPC)             │
    │          Sink (stdout, file, NATS)       │
    │          DLQ (failed events)             │
    └──────────────────────────────────────────┘
              │       (*  = feature-gated)
              │       (** = requires daachorse-index feature)
              ▼
     ┌────────────────────────┐
     │  EvaluationResult      │──> rule title, id, level, tags,
     │  = RuleHeader +        │    matched selections, field matches,
     │    ResultBody::        │    aggregated values, optional events
     │      Detection /       │
     │      Correlation       │
     └────────────────────────┘

A Mermaid version of this diagram is also available.

Performance

RSigma is designed for high-throughput detection. On an Apple M4 Pro:

Parsing: 12.7 MiB/s for 1000 rules
Detection: 1.06M events/sec (JSON, 100 rules)
Correlation: 569K events/sec (temporal + event-count)
Dynamic pipelines: 2.71M events/sec once built (no per-event overhead)

See BENCHMARKS.md for full Criterion results across all subsystems.

Reference

pySigma: reference Python implementation
Sigma Specification V2.1.0: formal specification
sigma-rust: Pratt parsing approach
sigmars: correlation support patterns
sigma_engine: official SigmaHQ Rust library for parsing and matching Sigma rules against events
pySigma-backend-sqlite: SQLite backend for pySigma (inspiration for the PostgreSQL backend)
pySigma-backend-athena: AWS Athena backend for pySigma (SELECT fields, CTE-based correlation, sliding window patterns)

Releasing

All crates share a single version (set in the workspace Cargo.toml) and are published together.

Publishing a new version

Bump the version in the root Cargo.toml.
Commit, push to main.
Create a GitHub Release (e.g. tag v0.2.0). The publish.yml workflow triggers automatically and publishes all crates in dependency order.

Dry run

Trigger the workflow manually via Actions → Publish to crates.io → Run workflow. Manual runs automatically pass --dry-run to every cargo publish invocation.

Recovering from a partial failure

If the workflow fails midway (e.g. rsigma-parser was published but rsigma-eval failed), re-running the workflow will fail at the already-published crate. To recover, publish the remaining crates manually in order:

# Skip crates that were already published successfully
cargo publish -p rsigma-eval && sleep 30
cargo publish -p rsigma
cargo publish -p rsigma-lsp

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 600 Commits
.cargo		.cargo
.github		.github
assets		assets
crates		crates
docs		docs
editors/vscode		editors/vscode
fuzz		fuzz
support		support
tests/fixtures/dynamic-pipelines		tests/fixtures/dynamic-pipelines
.dockerignore		.dockerignore
.gitignore		.gitignore
BENCHMARKS.md		BENCHMARKS.md
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
deny.toml		deny.toml
mkdocs.yml		mkdocs.yml

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RSigma

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RSigma

Supported Features

Crates

Installation

Docker

Quick Start

Daemon Input Modes

NATS Streaming

TLS Termination

Input Formats and Pipelines

Dynamic Pipelines

Rule Conversion

Library Usage

Streaming Runtime

Observability

Architecture

Performance

Reference

Releasing

Publishing a new version

Dry run

Recovering from a partial failure

License

About

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