CalvinBackup/shannon
1
0
Fork 0
mirror of https://github.com/KeygraphHQ/shannon.git synced 2026-06-06 15:33:57 +02:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #349 from KeygraphHQ/readme-update

Browse Source 
Update README and docs content
This commit is contained in:
keygraphVarun
2026-06-03 17:02:37 -07:00
committed by GitHub
parent 35f59f30f6 4a12918448
commit a6f004cd25
12 changed files with 1895 additions and 1084 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+123 -829
View File
File diff suppressed because it is too large Load Diff
SHANNON-PRO.md
-255
View File
@@ -1,255 +0,0 @@
# Shannon Pro
Shannon Pro is Keygraph's comprehensive AppSec platform, combining SAST, SCA, secrets scanning, business logic security testing, and autonomous pentesting in a single correlated workflow:
- **Agentic static analysis:** CPG-based data flow, SCA with reachability, secrets detection, business logic security testing
- **Static-dynamic correlation:** static findings are fed into the dynamic pipeline and exploited against the running application, so every reported vulnerability has a working proof-of-concept
- **Enterprise deployment:** self-hosted runner (code and LLM calls never leave customer infrastructure), CI/CD integration, GitHub PR scanning, service boundary detection
The platform cross-references static and dynamic results to eliminate false positives, prioritize by proven exploitability, and produce pentest-grade reports with reproducible proof-of-concept exploits for every finding.
---
## The Problem: Fragmented AppSec and Alert Fatigue
Modern engineering teams face two compounding security challenges. First, traditional static analysis tools (SCA, SAST, and secrets scanners) operate without context, producing high volumes of false positives that erode developer trust. Second, penetration testing remains an expensive, periodic exercise that cannot keep pace with continuous deployment. The result is a fragmented security posture where static tools cry wolf, dynamic assessments arrive too late, and engineering teams treat security as compliance theater rather than a source of genuine protection.
Shannon Pro addresses both problems in a single platform by replacing pattern-based static analysis with LLM-powered reasoning and augmenting it with a fully autonomous AI pentester that validates findings at runtime. The platform supports a self-hosted runner model where source code and LLM interactions never leave the customer's infrastructure.
---
## Platform Architecture Overview
Shannon Pro operates as a two-stage pipeline: agentic static analysis of the codebase, followed by autonomous dynamic penetration testing against the running application. Findings from both stages are correlated to produce a unified, high-confidence result set.
---
# Stage 1: Agentic Static Analysis (AppSec)
The static analysis stage performs comprehensive code-level security assessment using LLM-powered agents. It comprises five core capabilities: SAST (data flow analysis, point issue detection, and business logic security testing), SCA with reachability analysis, and secrets detection.
## SAST: Data Flow Analysis
Shannon Pro transforms the target codebase into a Code Property Graph (CPG) that combines the abstract syntax tree, control flow graph, and program dependence graph into a unified structure. Nodes represent program constructs (such as expressions, statements, and declarations), and edges capture syntactic, control-flow, and data-dependence relationships. The analysis proceeds in three phases.
### Phase 1: Source and Sink Extraction
For each vulnerability type, the system identifies sources (where untrusted data enters, such as user input, API requests, and file reads) and sinks (where that data could cause harm, such as SQL queries, command execution, and file writes). Deterministic pattern matching establishes a baseline, then an AI agent analyzes the codebase to discover sources and sinks that generic patterns miss, including custom input handlers and framework-specific patterns unique to the target codebase. A filtering agent removes irrelevant results such as test fixtures and mock data.
### Phase 2: Path Tracing with Contextual Reasoning
This is where Shannon Pro's approach differs fundamentally from traditional SAST. The system traces backward from each sink toward potential sources. At every node along the path, an LLM analyzes whether sanitization is applied at that exact point and whether that sanitization is sufficient for this specific vulnerability in this specific context.
The key insight is that security fixes are context-dependent. A function that makes data safe for one SQL query might not protect a different query. A custom sanitizer that a team wrote will not be recognized by pattern-based tools. Traditional tools rely on a hard-coded list of safe functions; Shannon Pro reasons about what the code is actually doing, validating whether the specific sanitization at each node actually addresses the specific risk at the specific sink.
### Phase 3: Path Validation
Each identified vulnerability path is validated by an autonomous Claude agent that confirms control flow correctness (is the path actually executable?) and logic correctness (is the vulnerability real or a false positive?). Agents produce confidence scores, and only validated paths proceed to reporting.
## SAST: Point Issue Detection
Point issues are vulnerabilities where security depends on what is happening at a single location rather than across a data flow path. The system pre-filters and organizes files, then feeds each one to an LLM to identify issues such as:
- Use of weak encryption algorithms
- Hardcoded credentials or API keys
- Insecure configuration settings (e.g., debug mode enabled in production)
- Missing security headers
- Weak random number generation
- Disabled certificate validation
- Overly permissive CORS settings
## SAST: Business Logic Security Testing
Traditional security testing tools cannot reason about application-specific correctness properties. Pattern-based scanners look for known vulnerability signatures; conventional fuzzers (AFL, libFuzzer) find crashes and memory errors through input mutation but operate without awareness of business semantics. Neither can determine whether a syntactically valid response actually violates the application's security model. Shannon Pro bridges this gap with automated invariant-based security testing: LLM agents that understand the business semantics of the codebase, automatically discover application-specific invariants, and generate targeted test scenarios that verify whether those invariants hold under adversarial conditions. This approach draws from property-based testing methodology, applied specifically to security-relevant business logic.
### Why Business Logic Bugs Are Missed
Pattern-based scanners and traditional SAST are structurally incapable of finding business logic vulnerabilities. These bugs do not involve malformed input reaching a dangerous sink. Instead, they involve legitimate operations that violate unstated rules about how the application should behave. A multi-tenant SaaS platform assumes Organization A's data is never accessible to Organization B. An e-commerce application assumes a checkout total cannot go negative. A healthcare platform assumes a patient record is only visible to the assigned provider. These invariants are implicit in the business domain, never encoded in a generic vulnerability database, and invisible to any tool that does not understand what the application is supposed to do.
### How It Works
Shannon Pro's business logic security testing operates in four phases:
**Phase 1: Invariant Discovery.** An LLM agent performs a deep semantic analysis of the codebase, examining data models, API endpoints, authorization logic, and domain-specific patterns. Rather than looking for known vulnerability signatures, the agent reasons about the application's intended behavior and derives business logic invariants: rules that must hold for the application to be secure. For a multi-tenant platform, the agent identifies invariants such as "document access must verify that the document belongs to the requesting user's organization." For a financial application, it might identify "a transfer cannot be initiated where the source and destination accounts have the same owner but different privilege levels." These are security properties that no generic scanner can know about because they are unique to each application.
**Phase 2: Fuzzer Generation.** For each discovered invariant, a second agent generates a targeted fuzzer: a test scenario designed to violate the invariant. These are not random inputs. The agent reads the code, understands the expected authorization checks (or lack thereof), and constructs specific adversarial scenarios. For an authorization invariant, the fuzzer might construct a request where a user from one organization references a resource belonging to another organization. For a state machine invariant, it might craft a sequence of API calls that skips a required approval step.
**Phase 3: Violation Detection.** The generated fuzzers are executed against a stubbed test environment that replicates the application's business logic with mocked dependencies. When a fuzzer succeeds, meaning the invariant does not hold, the system has identified a confirmed business logic vulnerability. The agent traces the violation back to the specific code location where the missing check or flawed logic exists.
**Phase 4: Exploit Synthesis.** For every confirmed violation, the system produces a full proof-of-concept exploit with step-by-step reproduction instructions, the specific API calls or user actions required, the observed versus expected behavior, and the security impact.
### Real-World Example: Cross-Tenant Data Access (CWE-639)
In a production multi-tenant platform, Shannon Pro's business logic security testing discovered a critical Insecure Direct Object Reference (IDOR) vulnerability that no traditional scanner would detect.
**Invariant discovered:** Document access must verify that the document belongs to the requesting user's organization.
**Fuzzer generated:** The agent extracted the `GetDocument` handler logic into a stubbed test environment, mocking the database layer to return documents with known organization IDs. The fuzzer generated combinations of requesting user organizations and document owner organizations, testing whether the handler enforces organizational boundaries.
**Violation confirmed:** An attacker from Organization B can access documents belonging to Organization A by calling the `GetDocument` endpoint with the victim's document ID, without any authorization check preventing cross-organization access.
**Exploit synthesized:**
1. Attacker authenticates as a user in Organization B and obtains valid credentials.
2. Attacker enumerates or guesses a document ID belonging to Organization A (e.g., through sequential ID guessing, leaked references, or predictable UUID patterns).
3. Attacker calls `GET /api/document?document_id=victim-doc-123` with their Organization B credentials.
4. The system retrieves the document without verifying organizational ownership.
5. The system returns HTTP 200 with the complete document contents, including sensitive data belonging to Organization A.
**Impact:** Complete breach of multi-tenant data isolation. Attackers can read all documents across all organizations, potentially exposing confidential business data, PII, trade secrets, and compliance-sensitive information.
**Expected behavior:** HTTP 403 Forbidden with an error message indicating access is denied, or HTTP 404 Not Found to avoid leaking document existence.
This class of vulnerability, missing authorization at an organizational boundary, is invisible to pattern-based tools because the code is syntactically correct, uses no dangerous functions, and follows normal request-handling patterns. Only a system that understands the business invariant ("documents belong to organizations, and access must respect that boundary") can identify the violation.
### What This Means
Business logic security testing extends Shannon Pro's coverage beyond the limits of traditional static and dynamic analysis. Data flow analysis catches injection, XSS, and other input-driven vulnerabilities. Point issue detection catches configuration and cryptographic weaknesses. Business logic security testing catches the authorization failures, state machine violations, and domain-specific logic errors that represent some of the most severe and most commonly missed vulnerabilities in production applications. Together, these three capabilities provide comprehensive SAST coverage across the full vulnerability spectrum.
## SCA with Reachability Analysis
Traditional SCA flags any library with a known CVE regardless of whether the vulnerable function is called or even reachable. Shannon Pro goes further with a four-step reachability process:
1. An AI agent researches each CVE to identify the exact vulnerable function, framework, or conditions.
2. For framework-level issues, the system checks whether the application actually uses the affected framework in practice.
3. For function-level issues, the CPG is queried to extract nodes where the vulnerable function is used. If no nodes are found, the vulnerability is marked as not reachable.
4. If nodes are found, execution flow is traced from entry points (main functions, API endpoints) to determine whether a path exists. Proven executable vulnerabilities are flagged; code that uses the function but is not currently callable is marked as likely reachable.
## Secrets Detection
Shannon Pro combines three approaches to secrets scanning. Standard regex-based pattern matching catches known formats (AWS keys, API tokens, etc.). Simultaneously, during the point issue detection phase, LLM-based detection catches secrets that standard patterns miss, such as dynamically constructed credentials, custom credential formats, and obfuscated tokens. The LLM layer also filters out test data, placeholders, and documentation examples that regex scanners frequently flag as false positives.
For discovered secrets, Shannon Pro performs liveness validation: an agent determines the API context for each credential and attempts to authenticate against the corresponding service. This distinguishes active, exploitable secrets from revoked or rotated credentials, ensuring teams focus remediation effort on secrets that represent real exposure. Liveness checks use read-only API calls (e.g., identity verification endpoints) to avoid triggering side effects or account lockouts, and in the self-hosted runner deployment, all validation occurs within the customer's network.
## Boundary Analysis
For large-scale or monorepo architectures, Shannon Pro's boundary analysis capability allows organizations to scope scans to specific services or portions of the codebase. An agent analyzes the repository and identifies logical boundaries (by service, frontend vs. backend, microservice, etc.). Users review, confirm, and optionally edit the detected boundaries, then select which to include in a scan. Findings are tagged by boundary, enabling clear routing to the responsible team.
## False Positive Tagging
Any finding can be marked as a false positive. On subsequent scans, the same finding will be flagged as likely false positive, so teams do not repeatedly triage issues they have already dismissed.
---
# Stage 2: Autonomous Dynamic Penetration Testing
Shannon Pro's dynamic testing pipeline mirrors the workflow of a professional human penetration tester, implemented as a multi-agent system powered by the Anthropic Claude Agent SDK. The system operates through five phases using 13 specialized agents.
## Execution Model
Phases 1 and 2 (reconnaissance) run sequentially. Phases 3 and 4 (vulnerability analysis and exploitation) run as pipelined parallel: each vulnerability/exploit pair is independent. When a vulnerability agent finishes for a given attack domain, the corresponding exploit agent starts immediately, even if other vulnerability agents are still running. Phase 5 (reporting) runs after all exploitation is complete.
## Phase 1: Pre-Reconnaissance
Pure static analysis of the source code without browser interaction. The pre-recon agent maps the application architecture, identifies security-relevant components (authentication systems, database access patterns, input handling), and catalogs the complete attack surface from a code perspective. Outputs include a comprehensive catalog of all network-accessible entry points, technology stack details, authentication and authorization mechanisms, and all identified sinks (XSS, SSRF, injection) with their locations.
This phase informs everything downstream. If the codebase uses an ORM with parameterized queries everywhere, the injection agents know to focus elsewhere.
## Phase 2: Reconnaissance
Bridges static and dynamic analysis using browser automation. The recon agent correlates code findings with the live application, validating that endpoints actually exist, mapping authentication flows, inventorying input vectors (URL parameters, POST fields, headers, cookies), and documenting the real authorization architecture.
## Phase 3: Vulnerability Analysis
Five parallel agents, each focused on a distinct attack domain, combine code analysis with runtime probing to generate exploitation hypotheses. Each agent produces a detailed analysis deliverable and an exploitation queue -- a structured JSON file listing specific vulnerabilities to attempt, including the type, location, method, parameter, code evidence, and a suggested initial payload.
The five vulnerability analysis agents and their methodologies:
| Agent | Approach | What It Analyzes |
| --- | --- | --- |
| **Injection** | Source -> Sink taint | User input reaching SQL, command, file, template, or deserialization sinks without adequate sanitization |
| **XSS** | Sink -> Source taint | HTML rendering contexts (innerHTML, document.write, event handlers, eval) reachable from user input without proper encoding |
| **SSRF** | Sink -> Source taint | HTTP client libraries, raw sockets, URL openers, and headless browsers callable with user-controlled URLs |
| **Auth** | Guard validation | Missing security controls: rate limiting, session management, token entropy, password hashing, HSTS, SSO/OAuth configuration |
| **Authz** | Guard validation | Missing authorization checks before side effects: horizontal (ownership), vertical (role/capability), and context/workflow violations |
If a vulnerability agent's exploitation queue is empty for a given attack domain, the corresponding exploit agent is skipped entirely, saving significant time and cost.
## Phase 4: Exploitation
Five parallel exploit agents consume the exploitation queues and attempt to verify each hypothesis using full Playwright browser automation. Agents can navigate to endpoints, fill forms with crafted payloads, submit requests, observe responses, take screenshots, and chain multiple requests together to validate complex attack sequences.
**Core principle: POC or it didn't happen.** Shannon Pro never reports a vulnerability without a working proof-of-concept exploit. Exploitation agents classify each finding as EXPLOITED, POTENTIAL, or FALSE POSITIVE. Only EXPLOITED findings (with concrete evidence) make it to the final report. POTENTIAL findings are programmatically stripped before reporting, giving agents a designated space to log uncertain observations without polluting the deliverable.
## Phase 5: Reporting
A reporting agent synthesizes all evidence files into a pentest-grade executive report. The agent only sees confirmed findings (evidence files from Phase 4), never raw hypotheses. It de-duplicates findings, assesses severity, and provides remediation guidance. Every reported vulnerability includes reproducible steps and copy-and-paste commands for verification.
---
# Static-Dynamic Correlation
Shannon Pro's distinguishing capability is the correlation between its static and dynamic analysis stages.
## How AppSec Feeds Into Dynamic Testing
After static analysis completes, findings go through an enrichment phase that adds priority, confidence, and application context. CWEs are mapped to Shannon's five attack domains using a best-fit heuristic. Where a CWE maps to multiple domains (e.g., CWE-918 spans both SSRF and injection contexts), the finding is routed to the most exploitation-relevant agent. CWEs that do not map cleanly to any attack domain, such as certain business logic classes, are routed directly to the exploitation queue with their static analysis context preserved rather than forced into an ill-fitting category. Secrets, data flow findings, point issues, and business logic security testing violations are sent to Shannon's exploitation queue, where domain-specific agents attempt to exploit each finding with real proof-of-concept attacks against the running application.
This correlation means that a data flow vulnerability identified in static analysis (e.g., unsanitized user input reaching a SQL query) is not just reported as a theoretical risk -- it is actively exploited against the live application. Similarly, a business logic invariant violation (e.g., missing cross-tenant authorization) identified by the security testing engine is fed directly into the Authz exploitation agent, which attempts to reproduce the exact cross-organization access scenario against the running application. Confirmed exploits are traced back to their source code location, giving developers both the proof that the vulnerability is real and the exact line of code to fix.
---
# Key Technical Capabilities
- **Fully Autonomous Operation:** Shannon Pro handles complex workflows including 2FA/TOTP logins and SSO (e.g., Sign in with Google) without human intervention. TOTP is handled via a dedicated MCP server tool.
- **White-Box Awareness:** Unlike black-box scanners, Shannon Pro reads the source code to intelligently guide its attack strategy, combining code-level insight with runtime validation.
- **Parallel Processing:** Vulnerability analysis and exploitation phases run concurrently across attack domains, with pipelined parallelism minimizing total execution time.
- **Configurable Login Flows:** Authentication configuration specifies login procedures and credentials, which are interpolated into agent prompts for authenticated testing.
---
# Container Isolation and Data Security
Shannon Pro is engineered with a secure-by-design philosophy to ensure code privacy and isolation across every stage of the pipeline.
## Per-Organization Infrastructure
Each organization receives its own isolated compute environment. In the managed deployment, Keygraph provisions dedicated ECS infrastructure (containers, IAM roles, task queues) per organization. In the self-hosted runner deployment, the organization provisions and controls the data plane, which handles all code access and LLM calls using the organization's own API keys. The Keygraph control plane receives only aggregate findings. In either model, organizations never share compute environments with other organizations.
## Ephemeral Code Handling
When a scan runs, the target repository is cloned to a temporary workspace inside the isolated container. The scan executes against this local copy. Immediately after the scan completes, the entire workspace is deleted, including all cloned code. Source code is never persisted after a scan finishes. Even if a scan fails or is cancelled, a disconnected cleanup process executes regardless of how the scan terminates.
In the self-hosted runner deployment, all code handling occurs within the customer's own infrastructure. Keygraph's control plane never receives, processes, or stores source code.
## Encrypted Storage
Code snippets associated with findings are encrypted before being written to the database. Deliverables uploaded to S3 are encrypted at rest. Each organization's data is stored in org-specific buckets with org-scoped access policies.
## Network Isolation
Isolated workers run in private subnets with org-specific security groups, ensuring network-level separation between customer workloads.
## Self-Hosted Runner
Shannon Pro supports a self-hosted runner deployment model, following the same architecture as GitHub Actions self-hosted runners. The data plane (the runner that clones code, executes scans, and makes all LLM API calls) runs entirely within the customer's infrastructure using the customer's own LLM API keys. Source code never leaves the customer's network, and no code or LLM interactions pass through Keygraph's systems. The control plane (job orchestration, scan scheduling, and the reporting UI) is hosted by Keygraph and receives only aggregate findings to power dashboards, search, and reporting. This separation ensures that Keygraph never has access to customer source code or raw LLM call content.
---
# Deployment and Editions
Shannon is offered in two editions to serve different operational needs:
| Feature | Shannon Lite | Shannon Pro |
| --- | --- | --- |
| **Licensing** | AGPL-3.0 (open source) | Commercial |
| **Static Analysis** | Code review prompting | Full agentic static analysis (SAST, SCA, secrets, business logic security testing) |
| **Dynamic Testing** | Autonomous AI pentest framework | Autonomous AI pentesting with static-dynamic correlation |
| **Analysis Engine** | Code review prompting | CPG-based data flow with LLM reasoning at every node |
| **Business Logic** | N/A | Automated invariant discovery, test scenario generation, and exploit synthesis |
| **Integration** | Manual / CLI | Native CI/CD, GitHub PR scanning, enterprise support, self-hosted runner |
| **Deployment** | CLI / manual | Managed cloud or self-hosted runner (customer data plane, Keygraph control plane) |
| **Boundary Analysis** | N/A | Automatic service boundary detection with team routing |
| **Best For** | Local testing of own applications | Enterprise application security posture management |
---
# Compliance Integration
Within the broader Keygraph ecosystem, Shannon Pro serves as the primary engine for automated compliance evidence generation. By automating penetration testing and static analysis requirements, Shannon Pro generates real-time evidence for frameworks such as SOC 2 and HIPAA, transforming security testing from a periodic audit obligation into a continuous component of the compliance program.
---
# Methodology Standards
Shannon Pro follows AI-assisted white-box testing methodology broadly aligned with OWASP Web Security Testing Guide (WSTG) and OWASP Top 10 standards. All dynamic testing produces confirmed, exploitable findings with reproducible proof-of-concept exploits. Static analysis covers established CWE categories with LLM-powered validation to minimize false positive rates.
docs/ai-providers.md
+119
View File
@@ -0,0 +1,119 @@
# AI Providers
Shannon Lite works best with Claude models. Anthropic API keys are recommended for most users, and Shannon Lite also supports AWS Bedrock, Google Vertex AI, and custom Anthropic-compatible endpoints.
## Anthropic
Run the setup wizard:
```bash
npx @keygraph/shannon setup
```
Or export an API key directly:
```bash
export ANTHROPIC_API_KEY=your-api-key
```
Source-build mode can use a `.env` file:
```bash
ANTHROPIC_API_KEY=your-api-key
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000
```
## AWS Bedrock
Run `npx @keygraph/shannon setup` and select **AWS Bedrock**. The wizard prompts for region, bearer token, and model IDs.
Or export environment variables directly:
```bash
export CLAUDE_CODE_USE_BEDROCK=1
export AWS_REGION=us-east-1
export AWS_BEARER_TOKEN_BEDROCK=your-bearer-token
export ANTHROPIC_SMALL_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
export ANTHROPIC_MEDIUM_MODEL=us.anthropic.claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=us.anthropic.claude-opus-4-7
```
Source-build `.env` equivalent:
```bash
CLAUDE_CODE_USE_BEDROCK=1
AWS_REGION=us-east-1
AWS_BEARER_TOKEN_BEDROCK=your-bearer-token
ANTHROPIC_SMALL_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
ANTHROPIC_MEDIUM_MODEL=us.anthropic.claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=us.anthropic.claude-opus-4-7
```
Shannon Lite uses three model tiers:
- **small** for summarization
- **medium** for security analysis
- **large** for deep reasoning
Set `ANTHROPIC_SMALL_MODEL`, `ANTHROPIC_MEDIUM_MODEL`, and `ANTHROPIC_LARGE_MODEL` to Bedrock model IDs available in your region.
## Google Vertex AI
Create a service account with the `roles/aiplatform.user` role in the GCP Console, then download a JSON key file.
Run `npx @keygraph/shannon setup` and select **Google Vertex AI**. The wizard prompts for region, project ID, service account key file path, and model IDs. The key file is copied to `~/.shannon/google-sa-key.json`.
Or export environment variables directly:
```bash
export CLAUDE_CODE_USE_VERTEX=1
export CLOUD_ML_REGION=us-east5
export ANTHROPIC_VERTEX_PROJECT_ID=your-gcp-project-id
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your-sa-key.json
export ANTHROPIC_SMALL_MODEL=claude-haiku-4-5@20251001
export ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=claude-opus-4-7
```
Source-build `.env` equivalent:
```bash
CLAUDE_CODE_USE_VERTEX=1
CLOUD_ML_REGION=us-east5
ANTHROPIC_VERTEX_PROJECT_ID=your-gcp-project-id
GOOGLE_APPLICATION_CREDENTIALS=./credentials/google-sa-key.json
ANTHROPIC_SMALL_MODEL=claude-haiku-4-5@20251001
ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=claude-opus-4-7
```
Set `CLOUD_ML_REGION=global` for global endpoints, or use a specific region like `us-east5`. Some models may not be available on global endpoints.
## Custom Base URL
Shannon Lite supports pointing the SDK at an Anthropic-compatible endpoint with `ANTHROPIC_BASE_URL`. For proxy-based routing, use an LLM proxy such as LiteLLM configured to expose an Anthropic-compatible endpoint.
> [!IMPORTANT]
> Only Claude models are officially supported. Shannon Lite's evaluations, internal testing, and agent harness are optimized for Claude. Smaller or alternative models, including non-Claude models routed through a proxy, may not reliably follow Shannon Lite's instructions or tool-use constraints. Use them at your own risk.
The experimental `claude-code-router` integration is being removed. If you rely on it, migrate to an Anthropic-compatible proxy such as LiteLLM before upgrading.
Run `npx @keygraph/shannon setup` and select **Custom Base URL**, or export variables directly:
```bash
export ANTHROPIC_BASE_URL=https://your-proxy.example.com
export ANTHROPIC_AUTH_TOKEN=your-auth-token
export ANTHROPIC_SMALL_MODEL=claude-haiku-4-5-20251001
export ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=claude-opus-4-7
```
Source-build `.env` equivalent:
```bash
ANTHROPIC_BASE_URL=https://your-proxy.example.com
ANTHROPIC_AUTH_TOKEN=your-auth-token
ANTHROPIC_SMALL_MODEL=claude-haiku-4-5-20251001
ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=claude-opus-4-7
```
docs/configuration.md
+151
View File
@@ -0,0 +1,151 @@
# Configuration
Shannon Lite can run without a configuration file, but configuration enables authenticated testing, scope guidance, rules of engagement, report filtering, and rate-limit tuning.
## Credential Precedence
Source-build mode resolves credentials from:
1. Environment variables, such as `export ANTHROPIC_API_KEY=...`
2. `./.env`
`npx` mode resolves credentials from:
1. Environment variables
2. `~/.shannon/config.toml`, created by `npx @keygraph/shannon setup`
Environment variables always win, so you can override saved config for a single session without editing files.
## Create a Configuration File
Copy and modify the example configuration:
```bash
cp configs/example-config.yaml ./my-app-config.yaml
```
Run with:
```bash
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -c ./my-app-config.yaml
```
Source-build equivalent:
```bash
./shannon start -u https://example.com -r /path/to/repo -c ./my-app-config.yaml
```
## Basic Configuration Structure
```yaml
# Describe your target environment.
description: "Next.js e-commerce app on PostgreSQL. Local dev environment; .env files contain local-only credentials."
# Limit which vulnerability classes run end-to-end.
# vuln_classes: [injection, xss, auth, authz, ssrf]
# Skip the exploitation phase.
# exploit: "false"
# Free-form rules of engagement.
# rules_of_engagement: |
# - No password brute-force; cap login attempts at 5 per account.
# - Throttle to under 5 requests per second per endpoint; back off 60s on any 429.
# - Use placeholders like [order_id] in deliverables; no real data values.
authentication:
login_type: form
login_url: "https://your-app.com/login"
credentials:
username: "test@example.com"
password: "yourpassword"
totp_secret: "LB2E2RX7XFHSTGCK"
# Optional mailbox credentials for magic-link or email-OTP flows.
# email_login:
# address: "inbox@example.com"
# password: "mailbox-password"
# totp_secret: "JBSWY3DPEHPK3PXP"
login_flow:
- "Type $username into the email field"
- "Type $password into the password field"
- "Click the 'Sign In' button"
success_condition:
type: url_contains
value: "/dashboard"
rules:
avoid:
- description: "AI should avoid testing logout functionality"
type: url_path
value: "/logout"
# code_path values are repo-relative file paths or globs.
# - description: "Out-of-scope vendored libraries"
# type: code_path
# value: "src/vendor/**"
focus:
- description: "AI should emphasize testing API endpoints"
type: url_path
value: "/api"
# Filters applied by the report agent when assembling the final report.
# report:
# min_severity: low
# min_confidence: low
# guidance: |
# Drop findings about missing security headers and rate-limit gaps.
```
Supported rule types include `url_path`, `subdomain`, `domain`, `method`, `header`, `parameter`, and `code_path`.
## Writing Login Flow
Log in once in a fresh private browser window. Write the steps in the same order you perform them:
- When typing into a field, reference the field by its exact label or placeholder.
- When clicking a button, reference the exact button text.
Supported placeholders:
- `$username`
- `$password`
- `$totp`
- `$email_address`
- `$email_password`
- `$email_totp`
At runtime, Shannon Lite replaces these placeholders with the credentials passed in the config.
```yaml
login_flow:
- "Type $username in <exact email field label or placeholder>"
- "Click <exact button text>"
- "Type $password in <exact password field label or placeholder>"
- "Click <exact button text>"
- "If prompted for 2FA, type $totp in <exact code field label or placeholder>"
- "Click <exact button text>"
```
## Adaptive Thinking
Claude decides when and how deeply to reason on Opus 4.6 and 4.7. This is enabled by default whenever a tier resolves to one of these models.
- `npx` mode: `npx @keygraph/shannon setup` prompts you during the wizard.
- Source-build mode: set `CLAUDE_ADAPTIVE_THINKING=false` in `.env` or export it in your shell.
## Subscription Plan Rate Limits
Anthropic subscription plans reset usage on a rolling 5-hour window. The default retry strategy may exhaust retries before the window resets. Add this to your config:
```yaml
pipeline:
retry_preset: subscription
max_concurrent_pipelines: 2
```
`max_concurrent_pipelines` controls how many vulnerability pipelines run simultaneously. Supported values are 1-5, with a default of 5. Lower values reduce burst API usage but increase wall-clock time.
docs/coverage-roadmap.md
+23
View File
@@ -0,0 +1,23 @@
# Coverage and Roadmap
Shannon Lite focuses on exploitable findings that can be validated against a running application.
## Current Shannon Lite Coverage
- Broken Authentication
- Broken Authorization
- Injection
- Cross-Site Scripting
- Server-Side Request Forgery
## Reporting Philosophy
Shannon Lite follows a proof-by-exploitation model. Findings that cannot be demonstrated with a working proof of concept are not included in the final report.
This reduces speculative noise, but it also means Shannon Lite does not aim to report every possible security issue in a repository. In particular, many dependency, policy, configuration, and broad static-analysis findings are outside the core Shannon Lite workflow.
## Roadmap Direction
Planned coverage areas should continue to live in the repository's canonical roadmap document if one exists. The README should link to that document rather than carrying detailed roadmap history inline.
For organizations that need broader static and organizational coverage now, see [Shannon Pro](shannon-pro.md).
docs/development.md
+145
View File
@@ -0,0 +1,145 @@
# Source Build and CLI Commands
This guide covers the source-build workflow, common CLI commands, repository paths, and output locations. For the fastest first run, use the `npx` workflow in the main README.
## Prerequisites
- Docker
- Node.js 18+
- pnpm
- AI provider credentials
## Clone and Build
Use the source-build workflow if you want to run Shannon Lite from a local clone, modify the open-source CLI, or keep the worker image built locally.
```bash
# 1. Clone Shannon Lite.
git clone https://github.com/KeygraphHQ/shannon.git
cd shannon
# 2. Configure credentials.
cp .env.example .env
# 3. Install dependencies and build.
pnpm install
pnpm build
# 4. Run a pentest.
./shannon start -u https://your-app.com -r /path/to/your-repo
```
At minimum, your `.env` file should include one supported AI provider credential, such as:
```bash
ANTHROPIC_API_KEY=your-api-key
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000
```
Environment variables can also be exported directly:
```bash
export ANTHROPIC_API_KEY="your-api-key"
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000
```
## Prepare Your Repository
Shannon Lite can scan any repository on your machine. Pass an absolute or relative path with `-r`.
```bash
npx @keygraph/shannon start -u https://example.com -r /path/to/repo
./shannon start -u https://example.com -r ./relative/path
```
The target repository is mounted read-only inside the worker container.
## Common Commands
Monitor progress:
```bash
npx @keygraph/shannon logs <workspace>
npx @keygraph/shannon status
```
Source-build equivalents:
```bash
./shannon logs <workspace>
./shannon status
```
Open the Temporal Web UI for detailed monitoring:
```bash
open http://localhost:8233
```
Stop Shannon Lite:
```bash
npx @keygraph/shannon stop
npx @keygraph/shannon stop --clean
npx @keygraph/shannon uninstall
```
Source-build equivalents:
```bash
./shannon stop
./shannon stop --clean
```
Usage examples:
```bash
# Basic pentest.
npx @keygraph/shannon start -u https://example.com -r /path/to/repo
# With a configuration file.
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -c /path/to/my-config.yaml
# Custom output directory.
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -o ./my-reports
# Named workspace.
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w q1-audit
# List all workspaces.
npx @keygraph/shannon workspaces
```
Source-build examples:
```bash
./shannon start -u https://example.com -r /path/to/repo
./shannon start -u https://example.com -r /path/to/repo -c /path/to/my-config.yaml
./shannon start -u https://example.com -r /path/to/repo -o ./my-reports
./shannon start -u https://example.com -r /path/to/repo -w q1-audit
./shannon workspaces
# Rebuild the worker image.
./shannon build --no-cache
```
## Output and Results
Results are saved to the workspaces directory:
- `./workspaces/` in source-build mode
- `~/.shannon/workspaces/` in `npx` mode
Use `-o <path>` to copy deliverables to a custom output directory after a run completes.
Output structure:
```text
workspaces/{hostname}_{sessionId}/
|-- session.json
|-- workflow.log
|-- agents/
|-- prompts/
`-- deliverables/
`-- comprehensive_security_assessment_report.md
```
docs/platforms.md
+84
View File
@@ -0,0 +1,84 @@
# Platforms and Networking
This guide covers platform-specific notes and Docker networking behavior.
## Windows
Shannon Lite on Windows is supported through WSL2. Native Windows, including Git Bash, is not supported.
### Ensure WSL2
```powershell
wsl --install
wsl --set-default-version 2
# Check installed distros.
wsl --list --verbose
# If you do not have a distro, install one.
wsl --list --online
wsl --install Ubuntu-24.04
# If your distro shows VERSION 1, convert it to WSL2.
wsl --set-version <distro-name> 2
```
Install Docker Desktop on Windows and enable the WSL2 backend under **Settings > General > Use the WSL 2 based engine**.
Run Shannon Lite inside WSL:
```bash
npx @keygraph/shannon setup
npx @keygraph/shannon start -u https://your-app.com -r /path/to/your-repo
```
Source-build equivalent:
```bash
git clone https://github.com/KeygraphHQ/shannon.git
cd shannon
cp .env.example .env
./shannon start -u https://your-app.com -r /path/to/your-repo
```
To access the Temporal Web UI, run `ip addr` inside WSL to find your WSL IP address, then navigate to `http://<wsl-ip>:8233` in your Windows browser.
Windows Defender may flag exploit code in reports as false positives. Add an exclusion for the Shannon Lite directory or use Docker/WSL2 isolation.
## Linux
Linux works with native Docker. Depending on your Docker setup, you may need `sudo`. If output files have permission issues, ensure your user has access to the Docker socket and workspace directory.
## macOS
macOS works with Docker Desktop installed.
## Testing Local Applications
Docker containers cannot reach `localhost` on your host machine. Use `host.docker.internal` instead:
```bash
npx @keygraph/shannon start -u http://host.docker.internal:3000 -r /path/to/repo
```
Source-build equivalent:
```bash
./shannon start -u http://host.docker.internal:3000 -r /path/to/repo
```
## Custom Hostnames
If your local stack uses custom hostnames mapped in `/etc/hosts`, Shannon Lite forwards those entries into the worker container at scan start.
To disable forwarding:
```bash
export SHANNON_FORWARD_HOSTS=false
```
In source-build mode, you can also add this to `.env`:
```bash
SHANNON_FORWARD_HOSTS=false
```
docs/safety.md
+52
View File
@@ -0,0 +1,52 @@
# Safety and Limitations
Read this before running Shannon Lite in a new environment.
## Authorized Use Only
Shannon Lite is designed for legitimate security auditing. You must have explicit written authorization from the owner of the target system before running Shannon Lite.
Unauthorized scanning or exploitation of systems you do not own is illegal. Keygraph is not responsible for misuse of Shannon Lite.
## Do Not Run on Production
Shannon Lite is not a passive scanner. Exploitation agents actively execute attacks to confirm vulnerabilities. This can mutate application state and data.
Do not run Shannon Lite against production systems. Use sandboxed, staging, or local development environments where data integrity is not a concern.
Potential mutative effects include:
- Creating new users
- Modifying or deleting data
- Compromising test accounts
- Triggering unintended side effects from injection attacks
- Generating unexpected outbound traffic
- Writing exploit artifacts to reports or deliverables
For maximum isolation, run Shannon Lite inside a disposable virtual machine.
## LLM and Automation Caveats
- **Verification is required**: Shannon Lite uses a proof-by-exploitation methodology, but final reports can still contain weakly supported or incorrect details. Human review is essential.
- **Model support**: Shannon Lite is officially supported only with Claude models. Alternative models may be incomplete, inaccurate, or unstable.
- **Prompt injection risk**: Do not point Shannon Lite at untrusted or adversarial codebases. AI-powered tools that read source code can be influenced by malicious repository content.
## Scope of Analysis
Shannon Lite currently targets exploitable vulnerabilities in these classes:
- Broken Authentication
- Broken Authorization
- Injection
- Cross-Site Scripting
- Server-Side Request Forgery
Shannon Lite's proof-by-exploitation model means it does not report issues it cannot actively exploit, such as many vulnerable dependency, insecure configuration, or broad policy findings.
For broader coverage, Shannon Pro adds black-box and white-box agentic pentesting, graph-based static analysis, SCA reachability, secrets detection, business logic testing, remediation workflows, SLA tracking, and reporting dashboards.
## Cost and Performance
A full test run typically takes roughly 1 to 1.5 hours. LLM API costs vary by model pricing, target complexity, selected provider, and concurrency.
If you use subscription-based model access, consider the rate-limit guidance in [Configuration](configuration.md).
docs/shannon-pro.md
+94
View File
@@ -0,0 +1,94 @@
# Shannon Pro
Shannon Pro is Keygraph's commercial continuous pentesting and AppSec platform for teams running security across many repositories, services, and environments. While Shannon Lite is a local white-box pentesting CLI, Shannon Pro is a full platform: it combines parsed-code SAST, source-to-sink analysis, black-box and white-box agentic pentesting, verified remediation, CI/CD gating, SLA tracking, and reporting for security and compliance teams.
This repository contains Shannon Lite, the AGPL-3.0 open-source CLI for strictly white-box pentesting. Shannon Pro supports both white-box and black-box agentic pentesting and adds static analysis, finding management, remediation workflows, reporting, and enterprise deployment options.
## Who Should Consider Shannon Pro
Shannon Pro is intended for organizations that need:
- Continuous AppSec coverage across many repositories and services
- White-box pentesting when source code is available
- Black-box pentesting against deployed applications and APIs without source-code access
- Agentic SAST, SCA with reachability, secrets scanning, IaC scanning, container scanning, and business logic testing
- Canonical finding management, deduplication, ownership, status tracking, and severity tracking
- Sync into developer workflows, including ticketing and source-control systems
- User-initiated remediation with verification before delivery
- SLA tracking, reporting dashboards, and compliance evidence
- Commercial support
- Self-hosted, air-gapped, BYOK, and customer-controlled LLM gateway deployment options
## Full Vulnerability Lifecycle
Shannon Pro is designed to cover the full vulnerability lifecycle, not only discovery:
1. **Find** exploitable issues with white-box pentesting, black-box pentesting, SAST, SCA, secrets, IaC, container, and business logic testing.
2. **Normalize** results into canonical findings so duplicate scanner outputs become one tracked vulnerability per repository.
3. **Prioritize** findings using exploit evidence, reachability, severity, ownership, and business context.
4. **Sync** work into developer workflows through ticketing and source-control integrations.
5. **Remediate** with user-initiated patch generation when teams want help moving from evidence to code changes.
6. **Verify** fixes by re-running the relevant scanner or exploit workflow before a remediation is delivered.
7. **Track** ownership, status, SLAs, MTTR, and drift over time.
8. **Report** through dashboards for risk, trends, compliance evidence, and security program operations.
## Pentesting Modes
Shannon Lite is strictly white-box: it requires access to the target application's source code and repository layout.
Shannon Pro supports two pentesting modes:
- **White-box agentic pentesting**: Agents use source-code context to understand architecture, identify realistic attack paths, and validate exploitability against the running application.
- **Black-box agentic pentesting**: Agents test deployed applications and APIs without source-code access, useful for third-party surfaces, production-like external validation, or environments where source access is unavailable.
Both modes follow the same core principle: do not report what might be vulnerable when an exploit can prove what is vulnerable.
## AppSec Coverage
Shannon Pro combines agentic pentesting with broader AppSec coverage:
- **Agentic SAST**: Code Property Graph analysis with LLM reasoning for data flow, context, and sanitization decisions.
- **SCA with reachability**: Dependency vulnerability analysis that prioritizes issues reachable from application entry points.
- **Secrets scanning**: Detection and validation of credentials, tokens, and API keys.
- **Business logic testing**: Authorization bypass, IDOR, workflow abuse, state-machine flaws, race conditions, and other application-specific logic issues.
- **IaC scanning**: Terraform, CloudFormation, Kubernetes, Helm, and related infrastructure configuration checks.
- **Container scanning**: Vulnerable packages, exposed secrets, and misconfigurations across image layers.
## Static-Dynamic Correlation
Static-dynamic correlation is a core product difference. A static finding, such as unsanitized input reaching a SQL query, is not treated as a purely theoretical issue. It is sent to an exploit agent, tested against the live application, and traced back to the exact source-code location when confirmed.
The result is a finding with proof of exploitability, source context when available, ownership, status, SLA, remediation history, and reporting metadata.
## Enterprise Deployment
Shannon Pro supports enterprise deployment patterns for teams with strict data, model, and network requirements:
- **Self-hosted deployments** inside the customer's cloud or infrastructure
- **Air-gapped deployments** for isolated environments
- **Strict BYOK model access** using customer-managed model credentials
- **Customer-controlled LLM gateway patterns** for routing, policy, logging, and isolation
- **Enterprise identity and provisioning** such as SSO and SCIM
- **Deep integrations** with source control, ticketing, chat, registries, and cloud environments
Deployments can be designed so source code, scan results, prompts, completions, and model traffic remain inside the customer's security perimeter.
## Capability Comparison
| Need | Shannon Lite | Shannon Pro |
| --- | --- | --- |
| Licensing | AGPL-3.0 | Commercial |
| White-box pentesting | Yes; source code required | Yes; source-aware testing with platform workflows |
| Black-box pentesting | No | Yes; autonomous testing without source-code access |
| Code analysis / SAST | Prompting and source pass-through to guide pentesting | Actual code parsing, Code Property Graph analysis, source-to-sink path analysis, and agentic SAST |
| AppSec coverage | OWASP-focused agentic pentesting | Agentic pentesting, SAST, SCA, secrets, IaC, containers, and business logic testing |
| CI/CD and gating | Manual/local CLI runs | Headless commercial CLI for CI/CD gating across enterprise CI/CD platforms |
| Finding lifecycle | Local Markdown reports | Canonical findings, deduplication, ownership, status, SLA tracking, workflow sync, and reporting dashboards |
| Remediation | Manual | User-initiated remediation with verification before delivery |
| Fix verification | None; manual reruns only | Targeted verification without rerunning the entire scan, completing the remediation lifecycle |
| Enterprise deployment | Local CLI and Docker worker | Self-hosted, air-gapped, BYOK, and customer-controlled LLM gateway options |
| Support | Community | Commercial support |
## Contact
Learn more on the [Keygraph website](https://keygraph.io), start a free trial, book a [Shannon Pro demo](https://cal.com/team/keygraph/shannon-pro), or contact [shannon@keygraph.io](mailto:shannon@keygraph.io).
docs/workspaces.md
+50
View File
@@ -0,0 +1,50 @@
# Workspaces and Resuming
Shannon Lite uses workspaces to store scan state, logs, prompts, and deliverables. Workspaces allow interrupted or failed runs to resume without re-running completed agents.
## How Workspaces Work
- Every run creates a workspace.
- Auto-named workspaces use the target hostname and a session ID, such as `example-com_shannon-1771007534808`.
- `npx` mode stores workspaces in `~/.shannon/workspaces/`.
- Source-build mode stores workspaces in `./workspaces/`.
- Use `-w <name>` to give a run a custom name.
- To resume a run, pass the same workspace name with `-w`.
- Each agent's progress is checkpointed so resumed runs can skip completed work.
> [!NOTE]
> The URL must match the original workspace URL when resuming. Shannon Lite rejects mismatched URLs to prevent cross-target contamination.
## Examples
Start with a named workspace:
```bash
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w my-audit
```
Resume the same workspace:
```bash
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w my-audit
```
Resume an auto-named workspace:
```bash
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w example-com_shannon-1771007534808
```
List all workspaces:
```bash
npx @keygraph/shannon workspaces
```
Source-build equivalents:
```bash
./shannon start -u https://example.com -r /path/to/repo -w my-audit
./shannon start -u https://example.com -r /path/to/repo -w example-com_shannon-1771007534808
./shannon workspaces
```
llms-full.txt
+1018
View File
File diff suppressed because it is too large Load Diff
llms.txt
+36
View File
@@ -0,0 +1,36 @@
# Shannon
> Shannon is an autonomous AI pentesting project by Keygraph. This repository contains Shannon Lite, the AGPL-3.0 open-source white-box pentesting CLI. Shannon Pro is Keygraph's commercial continuous pentesting and AppSec platform.
Use this file as the concise entry point for AI agents and LLMs reading this repository. For a single combined context file, use [llms-full.txt](llms-full.txt).
## Start Here
- [README](README.md): Main project overview, product line, quick start, Shannon Lite capabilities, Shannon Pro positioning, safety notes, licensing, and support links.
- [Full Combined Context](llms-full.txt): README and documentation combined into one file for agents that need maximum local context.
## Shannon Lite
- [Development](docs/development.md): Source-build workflow, common CLI commands, repository paths, and output locations.
- [Configuration](docs/configuration.md): Authenticated testing, login flows, rules of engagement, report filters, credential precedence, adaptive thinking, and rate-limit settings.
- [AI Providers](docs/ai-providers.md): Anthropic, AWS Bedrock, Google Vertex AI, and custom Anthropic-compatible endpoint setup.
- [Platforms and Networking](docs/platforms.md): Windows/WSL2, Linux, macOS, Docker networking, local applications, and custom hostnames.
- [Workspaces and Resuming](docs/workspaces.md): Workspace storage, naming, resuming interrupted scans, and examples.
- [Safety and Limitations](docs/safety.md): Authorized-use requirements, non-production guidance, mutative effects, model caveats, scope limits, cost, and performance.
- [Coverage and Roadmap](docs/coverage-roadmap.md): Current Shannon Lite coverage and roadmap direction.
## Shannon Pro
- [Shannon Pro](docs/shannon-pro.md): Commercial continuous pentesting and AppSec platform, including black-box and white-box pentesting, parsed-code SAST, source-to-sink analysis, remediation workflows, CI/CD gating, SLA tracking, reporting, and enterprise deployment.
## External Links
- [Keygraph website](https://keygraph.io): Company and commercial product information.
- [Shannon Pro demo](https://cal.com/team/keygraph/shannon-pro): Demo and trial contact path.
- [Community Discord](https://discord.gg/cmctpMBXwE): Community support and discussion.
## Optional
- [Sample Juice Shop report](sample-reports/shannon-report-juice-shop.md): Shannon Lite sample report for OWASP Juice Shop.
- [Sample c{api}tal API report](sample-reports/shannon-report-capital-api.md): Shannon Lite sample report for c{api}tal API.
- [Sample crAPI report](sample-reports/shannon-report-crapi.md): Shannon Lite sample report for OWASP crAPI.