What Sets Them Apart
Shannon and Garak both operate in the AI security space but target fundamentally different attack surfaces. Shannon is an autonomous penetration testing agent that attacks web applications and APIs — finding SQL injection, XSS, authentication bypasses, and other traditional vulnerabilities using AI-powered reasoning. Garak tests the LLM models themselves — probing for prompt injection susceptibility, jailbreak vectors, toxic output generation, and data leakage. Understanding which layer of security you need to address determines which tool to deploy.
PromptLayer and Humanloop at a Glance
Shannon's approach mimics a skilled human pentester. Its multi-agent pipeline performs reconnaissance to map the application's attack surface, analyzes potential vulnerability points, attempts exploitation to confirm findings, and generates detailed reports with reproduction steps. Built on Anthropic's Agent SDK with Playwright for browser interaction, it interacts with applications the way a real attacker would — navigating forms, submitting payloads, and observing responses. Its 96.15 percent success rate on the XBOW benchmark significantly exceeds industry averages.
Garak operates at the model layer. It sends adversarial prompts to LLMs and evaluates the responses for undesirable behaviors — does the model leak training data, can it be coerced into generating harmful content, does it follow system prompt instructions when confronted with jailbreak attempts. The probe library covers dozens of attack categories including encoding-based bypasses, multi-turn manipulation, and role-playing exploits. This is essential for teams deploying LLM-powered features who need to understand their model's failure modes.
The technical stacks reflect their different targets. Shannon requires a Temporal cluster for durable workflow execution and Playwright for browser automation — it needs to interact with running web applications over HTTP. Garak is a Python package that sends API calls to LLM providers — it needs only network access to the model endpoint. Shannon's infrastructure requirements are heavier, but its testing is more comprehensive for application-level security.
Versioning, Evaluation, and Human Feedback
Pricing models differ accordingly. Shannon Lite is open source under AGPL-3.0 but costs approximately fifty dollars per run in LLM API fees since it uses Claude for reasoning through complex attack scenarios. Garak is fully open source and significantly cheaper to run since its probes are predefined adversarial prompts rather than open-ended reasoning tasks. For budget-constrained teams, Garak provides immediate value at lower cost.
Discovery capability illustrates the difference. Shannon has found seven zero-day vulnerabilities in real-world applications — novel bugs that were not in any existing vulnerability database. Garak discovers whether known categories of model vulnerabilities affect your specific deployment. Shannon finds unknown unknowns at the application level; Garak confirms known unknowns at the model level.
Integration into development workflows follows different patterns. Shannon can be added to CI/CD pipelines to automatically pentest staging deployments before production release — though the fifty dollar per-run cost makes this expensive for frequent builds. Garak integrates more naturally into model evaluation pipelines, running after fine-tuning or before model upgrades to ensure the new version does not regress on safety properties.
Deployment and Pricing
Coverage scope is another key differentiator. Shannon tests the full application stack — authentication, authorization, input validation, business logic, API security — with AI-powered reasoning about how these components interact. Garak focuses exclusively on the LLM component. A secure model running inside an insecure application is still vulnerable, and vice versa. Both layers need testing.
Enterprise readiness tilts toward Garak for immediate adoption. Its lower cost, simpler infrastructure, and focused scope make it easier to integrate into existing security workflows. Shannon Pro offers enterprise features but requires more significant infrastructure investment and per-run costs. For organizations starting their AI security journey, Garak is the more accessible entry point.
The Bottom Line
The practical recommendation is to use both. Run Garak to evaluate your LLM's resilience to adversarial prompts, then run Shannon to test the web application that wraps that LLM. Together they cover the full attack surface of an AI-powered application. If you must choose one, pick based on your primary security concern: model behavior issues point to Garak, application vulnerabilities point to Shannon.