Shannon vs Garak — AI Penetration Tester vs LLM Vulnerability Scanner

Shannon and Garak both address AI security but from completely different angles. Shannon is an autonomous pentester that attacks web applications and APIs to find real vulnerabilities, while Garak probes LLM models themselves for prompt injection, jailbreaks, and alignment failures. They are complementary tools targeting different layers of the AI application stack.

analyzed by Raşit Akyol April 1, 2026 updated September 5, 2026

Shannon garakwinner

Shannon review

Verdict

Garak secures the win as the widely adopted security benchmark for generative AI red-teaming and automated vulnerability assessments. With an exhaustive catalog of modular probes, detectors, and attack simulations, Garak enables security engineers and developers to stress-test language models against adversarial attacks, jailbreaks, and sensitive data extraction. While Shannon brings valuable specialized analysis, Garak's breadth, active open-source community, and CI/CD integration make it the foundational security auditing tool for LLM deployments. Our pick: garak.

Quick Comparison

Feature	Shannon	garak
Pricing	100% open-source and free autonomous AI penetration testing agent developed by Keygraph ($0 software license fee). Self-hosted via Docker with support for automated CI/CD security pipelines. Scan costs depend on user-provided LLM API token consumption (typically $10 to $50 per comprehensive full-application test run depending on code complexity). Commercial licensing and enterprise support are available directly from Keygraph.	garak is a 100% open-source LLM vulnerability scanner developed by NVIDIA and the open-source community, released under the Apache 2.0 license. It is completely free to use and automate in CI/CD pipelines.
Pricing Model	Freemium	Free
Platforms	Linux, macOS, and Windows-capable deployment. Requires authorized source/application access and AI provider credentials; exact runtime setup depends on Shannon Lite or Shannon Pro.	Python, CLI, any LLM endpoint
Open Source	Yes	Yes
Telemetry	Clean	Clean
Status	Active	Active
Editorial Pick	—	—
Last Verified	Sep 6, 2026	Aug 26, 2026
Description	Shannon is an autonomous white-box AI pentesting tool for web applications and APIs. It analyzes authorized source code, identifies attack vectors, attempts proof-by-exploitation, and produces remediation-ready reports. Shannon Lite is AGPL-3.0 for local use, while Shannon Pro is the commercial Keygraph platform for continuous security testing.	garak is NVIDIA's open-source LLM vulnerability scanner for red-teaming AI models and applications. Probes for prompt injection, data leakage, hallucination, toxicity, encoding-based attacks, and dozens of other vulnerability categories. Runs automated attack sequences against any LLM endpoint and generates detailed vulnerability reports. Features a modular probe/detector architecture that is extensible with custom attack patterns. Named after the Star Trek character known for deception.

Shannon

Pricing: 100% open-source and free autonomous AI penetration testing agent developed by Keygraph ($0 software license fee). Self-hosted via Docker with support for automated CI/CD security pipelines. Scan costs depend on user-provided LLM API token consumption (typically $10 to $50 per comprehensive full-application test run depending on code complexity). Commercial licensing and enterprise support are available directly from Keygraph.
Pricing Model: Freemium
Platforms: Linux, macOS, and Windows-capable deployment. Requires authorized source/application access and AI provider credentials; exact runtime setup depends on Shannon Lite or Shannon Pro.
Open Source: Yes
Telemetry: Clean
Status: Active
Editorial Pick: —
Last Verified: Sep 6, 2026
Description: Shannon is an autonomous white-box AI pentesting tool for web applications and APIs. It analyzes authorized source code, identifies attack vectors, attempts proof-by-exploitation, and produces remediation-ready reports. Shannon Lite is AGPL-3.0 for local use, while Shannon Pro is the commercial Keygraph platform for continuous security testing.

garakwinner

Pricing: garak is a 100% open-source LLM vulnerability scanner developed by NVIDIA and the open-source community, released under the Apache 2.0 license. It is completely free to use and automate in CI/CD pipelines.
Pricing Model: Free
Platforms: Python, CLI, any LLM endpoint
Open Source: Yes
Telemetry: Clean
Status: Active
Editorial Pick: —
Last Verified: Aug 26, 2026
Description: garak is NVIDIA's open-source LLM vulnerability scanner for red-teaming AI models and applications. Probes for prompt injection, data leakage, hallucination, toxicity, encoding-based attacks, and dozens of other vulnerability categories. Runs automated attack sequences against any LLM endpoint and generates detailed vulnerability reports. Features a modular probe/detector architecture that is extensible with custom attack patterns. Named after the Star Trek character known for deception.

What Sets Shannon and Garak Apart

Shannon and Garak address security from fundamentally different layers of the modern technology stack. Shannon is an autonomous AI agent designed for dynamic web application penetration testing, vulnerability identification, and active exploit verification against full-stack web platforms and APIs. Garak (Generative AI Red-teaming & Assessment Kit) is a specialized, open-source vulnerability scanner engineered to probe Large Language Models and GenAI endpoints for hallucinations, jailbreaks, prompt injection, and guardrail bypasses.

Shannon acts as an autonomous external attacker auditing web applications—mapping endpoint topologies and discovering OWASP Top 10 flaws like broken authorization (IDOR). Garak functions as an automated red-teaming benchmark for the AI model layer itself.

Shannon and Garak at a Glance

Choose Garak if you are building, fine-tuning, or deploying generative AI applications and need an automated, reproducible vulnerability scanner to evaluate model safety, jailbreak resilience, and prompt security compliance inside CI/CD pipelines.

Choose Shannon if you need an autonomous agentic penetration tester to systematically audit web application security posture and conduct black-box security assessments.

LLM Vulnerability Red-Teaming vs Web Application Pentesting

Garak implements a modular Python architecture consisting of generators (connecting to LLM endpoints), probes (dispatching hundreds of specialized attack vectors), and detectors (evaluating model outputs via rule-based and neural classifiers).

Shannon utilizes an autonomous multi-step reasoning agent paired with headless browser automation, HTTP interception proxies, and dynamic payload injectors to navigate authenticated sessions and validate exploitability.

Developer Experience and CI/CD Security Gates

Garak provides a streamlined command-line interface tailored for automation, outputting structured JSONL reports, HTML summaries, and standard exit codes that integrate seamlessly into GitHub Actions or GitLab CI security gates.

Shannon delivers an interactive pentesting experience designed for AppSec teams, generating executive risk summaries and step-by-step vulnerability reproduction steps.

The Bottom Line

Garak is the overall winner as the industry-standard, battle-tested open-source vulnerability scanner and red-teaming framework for LLMs and generative AI architectures.

FAQ

How do the testing scopes of Shannon and Garak differ in the AI security domain?

Shannon is an autonomous AI penetration testing agent discovering, exploiting, and validating application-level and network-level security vulnerabilities (OWASP Top 10 web flaws, authentication bypasses, SSRF) across full-stack software targets. Garak is an LLM vulnerability scanner and red-teaming framework evaluating models against prompt injections, jailbreaks, data leakage, and toxic outputs.

How do the underlying execution and attack simulation methodologies compare between Shannon and Garak?

Garak employs a modular test-harness architecture (generators, probes, detectors) firing deterministic and fuzz-generated adversarial payloads at an LLM API. Shannon uses an agentic reasoning loop with dynamic attack graph exploration, executing active penetration tools (port scanners, web fuzzers) and chaining multi-step exploits.

How do Shannon and Garak integrate into enterprise DevSecOps and continuous security pipelines?

Garak functions like an 'nmap for LLMs' in CI/CD pipelines executing fast, reproducible benchmark suites against model weights or APIs to detect safety regressions. Shannon operates as an automated DAST and red-team agent running in staging environments, performing prolonged penetration tests and generating exploit reproduction steps.

Which tool is required when auditing an LLM-powered application for both model safety and backend infrastructure vulnerabilities?

Both serve complementary layers: Garak probes the LLM layer for prompt injection susceptibility, system prompt extraction, and safety filter efficacy. Shannon tests the surrounding application infrastructure—API endpoints, tool-use sandboxes, database access controls, and RCE vectors exposed by agentic tools.

Sources & verification

Sources checked: September 5, 2026
Content verified: September 5, 2026

Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.