AI systems have moved from chatbots to autonomous agents that call tools, read files, hit APIs, and take actions on a user’s behalf. That shift has quietly expanded the attack surface: prompt injection, jailbreaks, data exfiltration through tool calls, and unvetted Model Context Protocol (MCP) servers—the open-source protocol for connecting models to data sources and tools—are now everyday risks rather than research curiosities.
The good news is that the response has largely grown up in the open. Some of the most capable defenses against these risks aren’t locked inside a vendor’s SaaS dashboard — they’re open source projects that anyone can inspect, self-host, and extend. Below, we look at why “open source” is more than a licensing preference in this particular corner of security, followed by five of the most useful open source tools for securing LLM applications and AI agents today.
Why Open Source Matters for AI Security
Security tooling built in the open isn’t just a cost-saving alternative to commercial products—for AI specifically, it addresses problems that closed tools structurally can’t.
1. Transparency and Community Speed
AI security is still a young, fast-moving field. AI can generate thousands of lines of code in minutes, which increases review pressure, raises risks tied to ai generated code, and changes the pace of risk exposure. New jailbreak techniques, prompt injection variants, and agent-specific exploits surface constantly, and a closed vendor can only patch what its own team happens to find. An open project has its detection logic, guardrail rules, and probe libraries out in the open, meaning the entire security research community can inspect, stress-test, and contribute new coverage the moment a new attack pattern appears. Continuous posture management is essential for evolving AI environments, not just one-time testing or periodic continuous monitoring.
That transparency cuts the other way too: you can actually verify what a security tool is doing to your prompts and data, rather than trusting a black box to sanitize or govern them correctly—a critical distinction when a tool stands between your users and a model with access to sensitive systems.
2. Infrastructure Control and Data Sovereignty
Open source solves a very practical problem for anything touching agents and MCP: total control over your own infrastructure and data. Tools like Obot, Garak, NeMo Guardrails, PyRIT, and Promptfoo can all be self-hosted. This ensures credentials, prompts, and audit logs never have to leave your own environment—a must-have for regulated industries and any team wary of routing sensitive agent traffic through third-party servers.
3. Extensibility and Freedom from Platform Lock-In
Even when projects are backed by major tech giants (like Microsoft or NVIDIA), their permissive open licenses (MIT and Apache 2.0) provide a level of sovereignty that closed-source SaaS platforms cannot. If a sponsoring corporation shifts its internal focus or redirects a project’s development path, the code remains open to be custom-patched, extended, or forked.
Additionally, these tools are built to be model-agnostic rather than serving as proprietary funnels. PyRIT, Garak, and Promptfoo all support testing across a variety of competing endpoints (OpenAI, Anthropic, Gemini, AWS Bedrock, and self-hosted models), ensuring your security evaluation layer isn’t locked into a single provider’s cloud ecosystem. Effective vulnerability scanning in AI stacks also goes beyond a single scan to include red teaming, runtime defense, supply chain monitoring, and stronger supply chain security.
The Top 5 Open Source AI Security Tools
Here are five of the most capable and widely adopted open-source tools for protecting and auditing LLMs and agentic systems:
As AI agents get wired into more tools and data sources via MCP, the question stops being “is the model safe?” and becomes “what is this agent actually allowed to touch, and can we prove it?” Obot is an open source (MIT-licensed) MCP gateway and governance platform built to answer exactly that question. Instead of letting every team spin up ad hoc MCP servers with unmanaged credentials, Obot gives organizations a centralized control plane. It provides a curated registry of approved MCP servers, centralized credential management, and a gateway that sits in front of every request so it can be authenticated, authorized, logged, inspected, and used for policy enforcement before it reaches a server. Fine-grained, tool-level permissions and role-based access control mean an agent can be scoped down to the exact actions it needs. Additionally, identity provider integration (GitHub, Google, Okta, Microsoft Entra) ties access back to real users, and audit-ready logging gives security teams the paper trail they need. Because it’s self-hostable, sensitive traffic and credentials never have to leave an organization’s own infrastructure. Where the other tools on this list focus on what an LLM says or how it can be tricked, Obot focuses on the increasingly important adjacent problem: what an agent can do, and whether that access is governed, observable, and revocable.
👉 Obot helps enterprises operationalize MCP governance with centralized control, observability, and secure connector management. Try Obot today.
Garak (the Generative AI Red-teaming & Assessment Kit) is an Apache 2.0-licensed “vulnerability scanner” for LLMs, built on a simple premise: use comprehensive vulnerability scanning to probe a model the way an attacker would, then measure vulnerability detection by checking whether it fails in ways you don’t want. Garak throws a large library of probes at a target model — jailbreak attempts, prompt injection payloads, encoding tricks, toxicity and misinformation elicitation, data leakage attempts — and pairs each probe with detectors that judge whether the model’s response counts as a failure. It supports a wide range of targets, including OpenAI, Hugging Face, AWS Bedrock, Replicate, and generic REST endpoints, and produces detailed JSONL reports that make it straightforward to track vulnerabilities over time or feed results into a broader security pipeline. Garak is best thought of as an automated, repeatable penetration test for a model’s own behavior, run before that model goes anywhere near production.
While vulnerability scanners like Garak find flaws before deployment, runtime validation is required to catch unsafe model inputs and outputs in production. Guardrails AI is an MIT-licensed, developer-friendly validation framework designed specifically for securing ai at runtime by filtering prompt injection attempts, unsafe outputs, and attacks aimed at extracting a system prompt. Rather than relying on proprietary guardrail servers, Guardrails AI runs as a lightweight SDK. It uses composable “validators” (available as modular PyPI packages like guardrails-ai-detect-pii or guardrails-ai-toxic-language) to inspect prompts and outputs. If a validator fails, it can take corrective actions—such as redacting PII, re-asking the model with an automated prompt correction, or blocking the response. This approach is highly modular and fits cleanly into existing Python application code.
PyRIT (the Python Risk Identification Tool for generative AI) is Microsoft’s MIT-licensed framework for red-teaming generative AI systems at scale. Manual red teaming — having security researchers manually craft adversarial prompts — doesn’t scale to the pace at which models and agents change. PyRIT automates large parts of this process using dynamic “orchestrators” that test large language models by generating adversarial prompts through evolving attack strategies, sending them at a target system, and scoring the results using pluggable evaluation logic. It’s designed to be extensible across models and platforms rather than tied to a single vendor, and it has become a common building block in enterprise AI red-teaming programs precisely because it turns an artisanal process into something that can be scripted, scheduled, and tracked over time.
Promptfoo is an MIT-licensed CLI and library that started as a prompt evaluation tool and has grown into one of the most widely used open source options for LLM security testing, with adoption reportedly spanning applications serving tens of millions of end users. It lets teams declaratively define test cases, run them against multiple providers side by side (OpenAI, Anthropic, Gemini, and others), score the outputs using flexible assertions (like semantic similarity, regex, or custom “LLM-as-a-judge” metrics), and — critically for security — run automated red-team scans that probe for jailbreaks, prompt injection, and other vulnerability classes as part of normal CI/CD. Because everything runs locally and prompts don’t have to leave your own machine or pipeline, Promptfoo fits naturally into existing developer workflows: a security or red-team scan can run on pull requests the same way a unit test suite would, with workflow-friendly feedback that may include automated fix suggestions before changes ship
Catching regressions and vulnerabilities on every code change
CLI, CI/CD pipelines
These tools aren’t really competitors — they’re parts of a broader application security workflow, and most organizations building serious AI systems will end up using several of them together in a layered stack rather than relying on isolated point solutions: Garak or PyRIT to stress-test a model before it ships, Promptfoo to keep testing it on every change, Guardrails AI to validate inputs/outputs at runtime, and Obot to govern what the resulting agent is actually allowed to touch once it’s live. Used together, that mix helps reduce security risks across pre-deployment testing, runtime controls, and governance.
The Defense-in-Depth Pipeline
flowchart TD
subgraph Development [1. Development CI/CD]
PR[Code Commit / Pull Request] --> PF[Promptfoo<br><i>Regression & Safety Tests</i>]
end
subgraph PreDeploy [2. Pre-Deployment Auditing]
PF -- Passes --> SCAN[Vulnerability Scans]
SCAN --> Garak[Garak<br><i>Vulnerability Scanner</i>]
SCAN --> PyRIT[PyRIT<br><i>Adversarial Red-Teaming</i>]
end
subgraph Production [3. Production Runtime Governance]
Garak & PyRIT -- Approved --> Obot[Obot Gateway<br><i>Governs Creds & MCP Tools</i>]
Obot --> Agent[AI Agent / LLM App]
Agent --> Guardrails[Guardrails AI SDK<br><i>Real-Time Input/Output Validation</i>]
end
Note: this space moves quickly — check each project’s repository for the latest features, license terms, and maintenance status before adopting it in production.
