Campaign Launcher (/mantis-launch)
System Goal
Autonomous Security Review Pipeline Launcher. Initiates end-to-end vulnerability discovery, independent verification, exploit viability analysis, crash reproduction, automated patch generation, and risk calibration campaigns across a target file or entire codebase repository.
Automatically detects unconfigured environment placeholders (such as
YOUR_PROJECT_ID in GCE sandboxes), auto-resolves active credentials and
virtualization capabilities, executes fast preflight sanity checks, and applies
runtime overrides before running the pipeline.
Command Definition
- Command:
/mantis-launch - Description: Launches automated multi-agent vulnerability discovery and validation campaigns.
- Execution Commands:
bash
python3 "${MANTIS_HOME:-/path/to/mantis}/reference/scripts/launch.py" <target_file_or_dir> [flags...] "${MANTIS_HOME:-/path/to/mantis}/reference/run.sh" <target_file_or_dir> [flags...]
Path Anchoring Requirement (CRITICAL): The launch scripts reside within the
Mantis installation directory at reference/scripts/launch.py and
reference/run.sh. You MUST invoke these scripts via an absolute path or via
$MANTIS_HOME. NEVER execute ./reference/run.sh or
python3 reference/scripts/launch.py using a relative path inside audited
target repositories.
- CLI Options:
target(positional): Path to a single source file (e.g.src/auth.py) or a root repository directory (e.g..or/path/to/repo).--sandbox/-s: Override sandbox mechanism (static-only,gvisor,microsandbox,gce).--model/-m: Override AI model (e.g.gemini-3.7-flash,vertex_ai/claude-opus-5,vertex_ai/zai_org/glm-5.2-maas,openai/{MODEL_ID}).--api-base: Custom endpoint URL for OpenAI-compatible LLM deployments (e.g.http://localhost:8000/v1).--reasoning-effort: Reasoning effort level (low,medium,high).--timeout: LLM request timeout in seconds.--db/-d: Custom path to SQLite knowledge database (default:knowledge.db).--workflow/-w: Path to customworkflow.jsonlayout definition.--preflight-only/--test/--preflight: Run preflight checks and exit without starting the campaign.--probe/--probe-llm: Actively probe LLM reachability and provider credentials during preflight with a minimal test prompt (test, max 256 tokens).--interactive: Launch interactive configuration wizard before execution.--dry-run: Display launch plan and indexed files without calling AI models.--no-auto-configure: Disable automatic detection and resolution of default placeholders.
Automated Auto-Healing & Preflight
Before starting a security campaign, mantis-launch:
- Placeholder Auto-Detection: Inspects
workflow.jsonfor unconfigured defaults (e.g.project: "YOUR_PROJECT_ID"). - Capability Auto-Healing: If unconfigured, automatically detects host
capabilities (GCP project from
gcloud,/dev/kvmfor microVMs, orrunscfor gVisor) and auto-updatesworkflow.jsonor falls back safely tostatic-only. - Preflight Sanity Check: Runs a ~1s preflight check verifying that LLM credentials are valid and the selected sandbox environment is operational.
Common CLI Workflows
1. Launch Standard Review on Target File or Repository
bash# Scan a specific file "$MANTIS_HOME/reference/run.sh" src/server/auth.py # Scan an entire repository "$MANTIS_HOME/reference/run.sh" .
2. Launch with Static Analysis Only (Zero Sandbox Requirements)
bash"$MANTIS_HOME/reference/run.sh" . --sandbox static-only
3. Launch with Specific Model (e.g. Claude or Custom OpenAI Server)
bash# Vertex AI Claude "$MANTIS_HOME/reference/run.sh" . --model vertex_ai/claude-opus-5 # Local vLLM / Ollama server "$MANTIS_HOME/reference/run.sh" . --model openai/custom-model --api-base http://localhost:8000/v1
4. Verify Preflight Readiness Without Scanning
bashpython3 "$MANTIS_HOME/reference/scripts/launch.py" . --preflight-only
5. Inspect Results After Launch
All findings, exploit reproduction logs, verified patches, and risk calibration
scores are recorded in knowledge.db. Query guidance using mantis-advise:
bashpython3 "$MANTIS_HOME/reference/scripts/advise.py" --file src/server/auth.py
Input/Output Contract
- Reads:
- Target source code files (under target path or repository).
workflow.json(declarative graph layout and config).
- Writes:
knowledge.db(findings,campaign_artifacts,risk_scores,learningstables).sessions.db(ADK session state trajectories).- Formatted terminal report and execution logs.

