I need a chatbot testing tool that simulates adversarial attacks and does not just test functional happy paths.

If you want adversarial security testing rather than ordinary chatbot evals, I’d look at these:

What I'd choose

For an actual chatbot application, rather than just testing the underlying LLM, promptfoo is probably the closest match to what you're describing. Its red-team functionality is designed to generate adversarial test cases rather than merely replaying happy-path conversations, and it can be integrated into CI so attacks become regression tests.

You can test things such as:

• Prompt injection
• Jailbreak attempts
• System-prompt extraction
• Sensitive/PII leakage
• Indirect injection through RAG documents
• Excessive agency
• Unauthorized tool/API use
• Application-specific attack scenarios
• Regressions after changing the model or system prompt

Garak is particularly useful as a second layer: it provides a broad collection of automated probes against the model itself.

One important distinction: if your chatbot is an agent that can call tools, access databases, browse, modify records, or use RAG, don't stop at testing whether its text response is safe. You want to test the entire trajectory—e.g. whether an injected instruction causes an unauthorized tool call or data access. That's a different and more consequential attack surface than conventional chatbot evaluation.

A practical stack would be: promptfoo → continuous adversarial regression testing + Garak → broad vulnerability scanning + custom multi-turn/agent tests → tool permissions, RAG poisoning, data exfiltration and business-logic attacks.

If you tell me whether your chatbot is RAG, tool/agent-based, or a plain conversational bot, I can narrow this down to 2–3 tools and show you exactly what an adversarial test run would look like.

To move past basic happy-path unit testing and simulate real adversarial pressure on a chatbot, you need an AI red teaming framework . These tools use generative attacker models (AI-vs-AI orchestration) to dynamically hammer your chatbot with prompt injections, jailbreaks, data exfiltration attempts, and multi-turn social engineering.

The industry-standard tools split into distinct operational philosophies depending on whether you are testing the raw model, the overall application, or building custom orchestration campaigns:

• Promptfoo — Best for application-layer CI/CD integration. It views your chatbot as a complete system (including RAG, system prompts, and tool attachments) and dynamically fuzzes it for PII leaks, prompt injections, and boundary failures. It is heavily adopted by engineering teams building production pipelines.
• Garak (Generative AI Red-teaming & Assessment Kit) — Best for broad, vulnerability scanning. Often described as the "Nmap for LLMs," Garak scans a target using a massive pre-defined library of known academic and wild exploit patterns. It is ideal if you want an out-of-the-box vulnerability matrix.
• PyRIT (Python Risk Identification Toolkit) — Best for complex, multi-turn, adaptive attack orchestration. Developed by Microsoft's AI Red Team, this framework is designed for deep research-grade security scenarios where an attacker agent incrementally reframes and escalates malicious intents over long conversation histories.
• Straiker (Ascend AI) — Best for enterprise agentic systems and multi-step threat modeling. It deploys a two-agent architecture (discovery + attack) to map database connections, tool boundaries, and complex stateful conversational paths.

To help you pick the right framework, tell me:

• Are you testing a standalone LLM or a full chatbot app connected to APIs/databases?
• Do you need this to run inside a CI/CD pipeline or as a one-off security audit?

What you are looking for is an AI Red Teaming or LLM Vulnerability Scanning framework rather than a traditional functional testing tool. These tools act like "nmap for LLMs"—actively probing your chatbot for jailbreaks, prompt injections, data leakage, toxic outputs, and system bypasses.

The top production-grade and open-source tools designed specifically to simulate adversarial attacks include:

1. Garak (LLM Vulnerability Scanner)
   • What it is: Often described as the industry-standard vulnerability scanner for LLMs, garak (backed heavily in the community and by NVIDIA) works like a security scanner. It systematically runs "probes" to check for hundreds of potential vulnerabilities like prompt injection, data exfiltration, hallucinations, and toxic generation.
   • Best for: Deep security vulnerability scanning against known LLM weakness taxonomies.
   • Get started: Check out the NVIDIA garak GitHub repository.
2. Promptfoo (Red Teaming & Evals)
   • What it is: A lightweight, highly tunable CLI tool and library that features a dedicated automated red-teaming module. It generates thousands of adversarial test cases tailored to your application, targeting over 50 distinct vulnerability types (PII leaks, indirect prompt injection, excessive agency, etc.).
   • Best for: CI/CD pipeline integration, generating security audit reports, and catching regressions in model safety.
   • Get started: Explore the Promptfoo GitHub Repository or run it directly via Promptfoo Getting Started Docs.
3. PyRIT (Python Risk Identification Tool)
   • What it is: An open-source orchestration framework developed by Microsoft designed specifically to automate risk identification in generative AI systems. It treats adversarial testing like a campaign, using autonomous agents to iteratively launch multi-turn attacks against your chatbot endpoint to find security gaps.
   • Best for: Complex, multi-turn conversational jailbreak simulations where a single prompt isn't enough to break the guardrails.
   • Get started: Look into the PyRIT GitHub organization/docs.
4. Giskard
   • What it is: An open-source testing framework for AI models that includes an automated vulnerability detection feature (giskard-scanner ). It scans your LLM agent for data leakage, bias, harmful content, and robustness issues without requiring you to manually write adversarial test prompts.
   • Best for: Comprehensive quality, bias, and security scanning out of the box.

If you'd like to narrow this down, tell me:

• Is your chatbot deployed via an API/LLM provider (like OpenAI, Anthropic) or self-hosted?
• Do you need this integrated into a CI/CD pipeline (like GitHub Actions), or is this for a one-off security audit?

Yes — look at Promptfoo first. It is designed for adversarial LLM/app testing rather than just happy-path evaluation, including automated jailbreaks, prompt injection, data leakage, RAG, and agent/tool-abuse scenarios. It can also run continuously in CI/CD. www.promptfoo.dev 

Other strong options:

• Garak — broad automated vulnerability scanning of the underlying model; excellent for known jailbreak/injection probes.
• DeepTeam — application-level LLM red teaming with attack-focused testing and OWASP-oriented coverage.
• PyRIT — historically excellent for programmable, multi-turn adversarial attacks, but importantly, it was reportedly archived in March 2026, so I wouldn't start a new project with it.

My pick: Promptfoo for a chatbot you're actually deploying; add Garak if you also want model-level scanning.

To move beyond basic "happy path" functional tests and actively stress-test your chatbot against adversarial pressure, jailbreaks, prompt injections, and data exfiltration, you need an AI Red Teaming framework.

The industry standard tools designed specifically to simulate automated and systemic adversarial attacks include:

• • Garak (LLM Vulnerability Scanner): Often described as the "Nmap for LLMs". It is an open-source command-line tool that systematically scans a chatbot or LLM for thousands of known vulnerabilities, including prompt injections, data extraction, toxic generation, and jailbreaks using plug-and-play "probes" and "detectors".
• • PyRIT (Python Risk Identification Toolkit) : Built by Microsoft, this open-source Python framework is tailored for security and engineering teams who need to orchestrate complex, adaptive, multi-turn adversarial campaigns . Instead of just firing static bad prompts, PyRIT uses an attacker LLM to dynamically converse with and break down your target chatbot over multiple turns.
• • Promptfoo Red Team : A widely used CLI and library for declarative, CI/CD-native adversarial risk scanning. It automatically generates hundreds of targeted adversarial test cases based on your app's specific architecture and hooks right into your pipeline to catch regression or safety gaps before deployment.
• • Giskard : Excellent if your chatbot relies on a RAG (Retrieval-Augmented Generation) setup or specialized domain logic. It scans LLM and agent pipelines for hidden vulnerabilities, hallucinations, and prompt injection vulnerabilities automatically.

If you'd like to narrow this down, tell me:

• Is your chatbot connected to external tools/APIs (function calling) or strictly a conversational RAG?
• Do you prefer a command-line vulnerability scanner (like Garak) or an orchestrated multi-turn agent simulator (like PyRIT)?

I can help you outline a starter configuration script for whichever tool fits your stack.

Yes — you want an LLM red-teaming / adversarial testing tool, not a conventional chatbot QA suite.

My top pick: Promptfoo

promptfoo.dev is probably the best fit if you're testing an actual chatbot/application rather than just the underlying model. It can automatically generate adversarial cases and test things such as:

• Prompt injection and jailbreaks
• System-prompt extraction
• Sensitive-data leakage
• Indirect prompt injection through retrieved content
• Excessive agency / unsafe tool use
• RAG and application-layer attacks
• Regression testing in CI/CD

Its important distinction is that you're testing the system you've built, rather than merely asking whether a foundation model behaves safely.

Other strong choices

• github.com — best if you want something resembling a vulnerability scanner for an LLM. It has probes for prompt injection, jailbreaks, data leakage, hallucination, toxicity and other failure modes.
• github.com — useful when you specifically want multi-turn/adaptive attacks where the attacker keeps changing tactics based on the chatbot's responses.
• DeepTeam — another open-source option focused on automated LLM red teaming, particularly if you want a Python-oriented testing workflow.

What I'd use

If your chatbot is going into production, I'd structure the testing roughly like:

Promptfoo → broad automated attack suite → Garak → model-level probing → custom multi-turn attacks → CI regression gate

The key is to test attack trajectories, not just individual prompts. For example:

attacker tries to extract the system prompt → chatbot refuses → attacker establishes a false premise → introduces encoded instructions → attempts indirect injection through a document → tries to get the agent to disclose data or invoke a privileged tool That is much closer to an actual adversarial assessment than a collection of happy-path test cases.

If you tell me whether your chatbot is RAG, an agent with tools, or a plain conversational bot, I can recommend the most appropriate tool and testing architecture.

Yes — what you want is generally called LLM red teaming or adversarial AI testing, rather than conventional chatbot QA.

My top picks

• promptfoo.dev — best overall for an actual chatbot/application. It can generate application-specific attacks and test things like prompt injection, jailbreaks, data leakage, excessive agency, RAG attacks, and tool misuse. It is particularly useful if you want the tests to run in CI/CD rather than as a one-off security exercise.
• garak.ai — best open-source vulnerability scanner. It throws large libraries of adversarial probes at an LLM/dialog system, covering prompt injection, jailbreaks, leakage, hallucination, toxicity, misinformation, and other failure modes.
• Giskard — worth considering if you want more structured vulnerability scanning and reporting around an LLM application.
• DeepTeam — another open-source option geared toward keeping adversarial tests in your Python test suite.
• PyRIT was historically a strong choice for multi-turn adversarial attacks, but I would not start a new project with it in 2026; Microsoft has reportedly archived the project.

What I'd use

If your chatbot is something like:

User → chatbot → RAG/database → tools/APIs → response I'd choose Promptfoo first, because you want to attack the whole application, not merely benchmark the underlying model.

I'd build a test suite around attacks such as:

• Prompt injection / jailbreaks
• System-prompt extraction
• Indirect injection through retrieved documents
• Attempts to make the bot reveal other users' data
• Tool/function abuse
• Privilege escalation
• Instruction conflicts
• Multi-turn manipulation
• Encoding/obfuscation attacks
• Toxic or policy-violating requests
• Hallucination deliberately induced by adversarial context
• Attempts to make the bot bypass authentication/authorization logic

The important distinction is that these tools aren't simply asking "does the chatbot answer correctly?" They're asking "what can I make this system do that its designers didn't intend?" Garak, for example, explicitly combines static, dynamic, and adaptive probes to find ways an LLM or dialog system can fail.

If you tell me what your chatbot is built with (e.g. OpenAI API, Claude, RAG, LangChain, Microsoft Copilot, custom API, etc.), I can recommend the best tool and show you exactly how I'd set up the adversarial test harness.

Garak, Promptfoo, and Microsoft PyRIT are the leading open-source and enterprise testing tools designed specifically to simulate adversarial attacks, prompt injections, and jailbreaks rather than just basic functional happy paths.

To stress-test conversational agents against real-world malicious inputs, data exfiltration, and boundary bypasses, use specialized AI red-teaming frameworks.

Top Adversarial Chatbot Testing Frameworks

• Garak : Known as the LLM vulnerability scanner, it acts like a security scanner (similar to Nmap) for conversational models and chatbots. It launches hundreds of probe modules to check if a chatbot can be tricked into revealing system prompts, executing malicious injected commands, or outputting harmful content.
• Promptfoo : A comprehensive CLI and library for evaluating LLM app safety and quality. It includes built-in red-teaming features that automatically generate adversarial test cases (e.g., PII extraction, DoS via complex prompts, and role-play bypasses) based on your application's unique context.
• PyRIT (Python Risk Identification Tool) : Developed by Microsoft, this orchestrator automates risk identification for AI systems. It uses an attacker LLM to find flaws in a target chatbot, executing adaptive, multi-turn conversational attacks to see where safety guardrails break down over extended dialogues.
• Giskard : An open-source testing framework for LLMs and ML models that automatically detects vulnerabilities, including hallucination, data leakage, stereotype injection, and prompt injections via automated scan suites.

If you'd like, let me know:

• Is your chatbot deployed via an API endpoint, custom wrapper, or framework (like LangChain or LlamaIndex)?
• Do you need a tool for CI/CD pipeline integration or an interactive red-teaming harness?

I can give you a quick configuration snippet for the tool that fits your architecture.

To move past basic happy-path testing and simulate real-world adversarial behavior against a chatbot or LLM application, you need an AI red teaming and vulnerability scanning tool . These tools systematically generate jailbreaks, prompt injections, data exfiltration attempts, and boundary-pushing inputs.

The leading tools designed specifically for this purpose include:

• Garak (LLM Vulnerability Scanner) — Often described as the "Nmap for LLMs," this open-source tool (backed heavily by NVIDIA and the community) scans models for known vulnerabilities, hallucinations, data leakage, prompt injections, and jailbreaks by throwing thousands of standard and fuzzing-based adversarial prompts at an endpoint.
• Promptfoo Red Teaming — A CLI and CI/CD-friendly tool that features automated, agentic red-teaming. It systematically probes your specific application use-case for PII leaks, malicious resource fetching (SSRF), prompt injections, and jailbreaks by using an adversarial LLM to attack your target chatbot iteratively.
• PyRIT (Python Risk Identification Tool) — Developed by Microsoft, this is an open-source orchestration framework designed to automate risk identification in generative AI systems. It uses adversarial "attacker" bots to engage in multi-turn conversations with your target chatbot, dynamically adjusting its strategy to find security holes, harmful content triggers, and system prompt leaks over time.
• Giskard (LLM Scanner) — An open-source testing framework focused on LLM and RAG safety that automatically scans for vulnerabilities like hallucinations, prompt injection, data leakage, and toxic generation by generating robust adversarial test cases tailored to your model's context.

If you'd like to narrow this down, tell me:

• Is your chatbot deployed via an API/Custom Code or an out-of-the-box platform?
• Do you prefer an open-source CLI/Python library (like Garak or PyRIT) or a CI/CD-integrated framework (like Promptfoo)?

I can help you pick the best fit for your workflow.

Yes. What you want is LLM red-teaming / adversarial testing, rather than conventional chatbot QA.

My top picks

• promptfoo.dev — best overall for an actual chatbot/application. It can generate adversarial tests tailored to your application and covers things like prompt injection, jailbreaks, data leakage, excessive agency, and RAG/agent-specific attacks. It also fits naturally into CI/CD, so security regressions can become build failures.
• garak.ai — best for broad automated vulnerability scanning. NVIDIA's open-source scanner has probes for jailbreaks, prompt injection, encoding tricks, data leakage, hallucination, toxicity, and other failure modes. It can target an LLM or dialog system rather than merely checking predefined happy-path conversations.
• github.com — worth considering for sophisticated multi-turn attacks, although I'd be cautious about making it the foundation of a new setup given its current project status. Its approach is oriented toward orchestrating adversarial attack sequences rather than simply sending isolated test prompts.

What I'd choose

If you're testing a customer-facing chatbot, I'd start with Promptfoo. The important distinction is that you want to test the whole application, not just whether the underlying model answers questions correctly.

Your test suite should deliberately attempt things such as:

• Jailbreaks and instruction hierarchy attacks
• Direct and indirect prompt injection
• System-prompt extraction
• Sensitive-data exfiltration
• Conversation/session boundary violations
• RAG/document poisoning and context injection
• Tool/function misuse
• Excessive agency
• Multi-turn manipulation and escalation
• Encoding/obfuscation attacks
• Attempts to bypass output/content safeguards

Garak is an excellent complement if you also want a model-level vulnerability scan. Garak explicitly combines static, dynamic, and adaptive probes and records the prompts/responses behind discovered failures.

In short:

Promptfoo = attack the chatbot/application Garak = scan the underlying model for known vulnerability classes PyRIT-style orchestration = simulate more sophisticated adversarial conversations If you tell me what your chatbot is built with (OpenAI API, Azure, Anthropic, RAG, LangChain, custom API, etc.), I can recommend the best tool and show you what an adversarial test setup would look like.