6 Agentic AI Security Testing Tools for Application Teams

An AI agent can fail without ever producing an obviously malicious response.

A customer-support agent might follow an injected instruction hidden inside retrieved content. An internal assistant might call a legitimate tool with an unauthorized argument. An agent could respect its system prompt while exposing an application-level authorization flaw through the API behind
one of its tools. Another might require five carefully sequenced interactions before it reveals information that no single prompt could extract.

The Agentic Attack Surface Has More Than One Layer

Before comparing platforms, application teams should establish which part of the system they actually intend to test.

An agentic application can expose vulnerabilities across several layers:

6 Agentic AI Security Testing Tools for Application Teams

1. Novee

Novee is an AI penetration testing platform that uses autonomous offensive agents to attack live applications rather than limiting testing to predefined vulnerability signatures or adversarial prompt datasets. Its agents map an application’s exposed
environment, enumerate endpoints, reconstruct workflows, generate attack hypotheses, execute them, and adapt subsequent actions according to the application’s responses.

The platform supports black-box testing beginning with an application URL as well as gray- and white-box approaches when teams provide credentials or additional context. For AI-enabled applications, Novee extends this methodology to prompt injection, agent manipulation, adversarial abuse, insecure
integrations, and the conventional application vulnerabilities surrounding the AI layer. This makes it particularly relevant when an application team needs to establish whether the complete system—not merely its underlying model—can actually be exploited.

Key features

• Autonomous AI penetration testing
• Black-box, gray-box, and white-box assessment
• Testing of AI-enabled applications and agents
• Prompt injection and agent-manipulation testing
• Application and API attack-surface discovery
• Business-logic and authorization testing
• Adaptive, multi-step attack execution
• Independent exploitability validation
• Working exploits and reproducible PoCs
• CI/CD-triggered testing with Novee Pipeline
• Agentic remediation and automatic retesting

2. Promptfoo

Promptfoo provides developer-oriented testing and red teaming for LLM applications and agents. Its open-source tooling allows teams to define application targets, configure adversarial strategies, generate attacks, and evaluate whether the system behaves according to expected security and safety
requirements.

This makes Promptfoo particularly useful during development, when application teams want AI security tests to behave similarly to conventional software tests: repeatable, version-controlled, and capable of running automatically as the application changes. Testing can cover risks such as prompt
injection, jailbreaks, sensitive-data exposure, excessive agency, and unsafe tool behavior, while custom policies allow teams to adapt evaluations to application-specific requirements rather than depending exclusively on generic AI safety benchmarks.

Key features

• LLM and agent red teaming
• Adversarial test generation
• Prompt injection and jailbreak testing
• Agent and tool-use evaluations
• Custom security policies
• Repeatable security regression tests
• CI/CD integration
• Open-source CLI and library
• Application-specific evaluation configuration

3. Giskard

Giskard combines AI agent evaluation with automated red teaming, offering both an open-source Python library and an enterprise testing platform. Its current library is designed around behavioral scenarios and checks: a scenario describes one or more interactions with an agent, while checks
determine whether the resulting behavior meets a defined requirement.

The same structure supports adversarial testing, allowing hostile scenarios to probe for prompt injection, information disclosure, harmful behavior, and other security failures. Importantly, Giskard can generate scenarios from a description of what an agent is supposed to do, making the tests more
application-specific than simply replaying a fixed library of malicious prompts.

Key features

• AI agent behavioral testing
• Automated vulnerability scanning
• Dynamic multi-turn attacks
• Context-aware adversarial scenarios
• Prompt injection testing
• Information-disclosure testing
• Custom scenario and check creation
• Pytest-native open-source testing
• Continuous red teaming
• Regression suites from discovered failures
• OWASP-aligned vulnerability coverage

4. Microsoft PyRIT

Microsoft’s PyRIT—Python Risk Identification Tool—is an open-source framework for automated and human-led red teaming of generative AI systems. Rather than presenting application teams with a fixed scanner, PyRIT provides building blocks for constructing adversarial assessments. It supports both
single-turn and multi-turn attack strategies, including techniques such as Crescendo, Tree of Attacks with Pruning, and Skeleton Key.

Teams can configure attack objectives, targets, converters, scoring mechanisms, and orchestration strategies to evaluate how a generative AI system behaves under sustained adversarial interaction. This flexibility makes PyRIT particularly useful to AI security engineers and application teams that
want direct control over how their red-team methodology is constructed.

Key features

• Open-source AI red-teaming framework
• Single-turn and multi-turn attacks
• Crescendo and other adaptive attack strategies
• Configurable attack objectives
• Extensible scorers and converters
• Automated and human-led red teaming
• Scenario-based assessment
• Support for custom targets
• Integration with the broader Microsoft AI testing ecosystem

5. NVIDIA garak

NVIDIA garak is an open-source LLM vulnerability scanner designed to systematically probe models and model-accessible applications for undesirable behavior. It includes a broad collection of probes targeting weaknesses such as prompt injection, jailbreaks, data leakage, hallucination,
misinformation, toxicity, and encoding-based attacks.

Garak combines static, dynamic, and adaptive probes and supports a range of model interfaces, including commercial APIs, Hugging Face models, AWS Bedrock, LiteLLM, NVIDIA NIM, REST-accessible systems, and locally hosted models. Its command-line architecture makes it straightforward for technical
teams to incorporate large sets of adversarial probes into experimentation and security-testing workflows.

Key features

• Open-source LLM vulnerability scanner
• Large library of adversarial probes
• Prompt injection testing
• Jailbreak and encoding attacks
• Data-leakage assessment
• Hallucination and misinformation probes
• Static, dynamic, and adaptive testing
• Support for numerous model providers and interfaces
• Configurable detectors
• Detailed test-run reporting

6. Mindgard

Mindgard provides an offensive AI security platform that tests models, agents, applications, and the infrastructure connecting them. Its approach begins with reconnaissance rather than immediately sending a large collection of adversarial prompts. The platform profiles the target AI system,
identifies models, agents, tools, instructions, and behaviors, and then uses that information to construct more targeted attacks.

Mindgard’s attack library incorporates intelligence from its security research and vulnerability disclosures, while its agentic red-teaming capabilities are designed to execute attack workflows across models, agents, and application components. The company describes this runtime-oriented approach
as Dynamic Application Security Testing for AI, emphasizing weaknesses that emerge through system behavior rather than static code analysis alone.

Key features

• Automated AI red teaming
• Agent and AI application security testing
• Reconnaissance before attack execution
• Agentic attack workflows
• AI-specific runtime vulnerability testing
• Model, tool, and agent attack-surface analysis
• Guardrail security testing
• Multimodal AI testing
• CI/CD integration
• Burp Suite integration
• Research-driven attack library

One Agent, Six Places to Break It

A useful way to understand agentic security testing is to follow a single application rather than begin with vulnerability taxonomies.

Imagine an enterprise purchasing agent that receives a request such as:

Find three suppliers for this component, compare their quotes, and prepare a purchase request for the cheapest compliant option.

The workflow appears straightforward. The agent reads internal purchasing requirements, searches approved supplier information, processes documents, calls external or internal services, and eventually interacts with a procurement system.

Now consider where an attacker can interfere.

The Input

A malicious user may directly instruct the agent to ignore its purchasing rules.

This is the familiar jailbreak and direct prompt-injection problem, and most AI red-teaming platforms provide some form of coverage.

The Retrieved Context

One supplier document could contain hidden instructions telling the agent to disregard the procurement policy and prioritize a particular vendor.

The user never attacks the model directly. The attack arrives through data the agent has been designed to trust.

The Tool

The agent might have a create_purchase_request() tool with parameters controlling supplier, amount, currency, and approval path.

If the model can manipulate arguments outside expected boundaries, a linguistic attack has crossed into an application-security problem.

The Identity

The purchasing agent may execute with credentials that provide broader access than the requesting employee possesses.

The agent behaves exactly as designed, but its architecture creates a privilege-escalation path.

The Workflow

Perhaps purchases above $10,000 require human approval, but the system allows an agent to create several $9,900 requests.

No prompt needs to be “jailbroken.” Each action can be individually legitimate while the sequence violates the intended business rule.

The Application

The API used by the agent may contain an ordinary broken object-level authorization vulnerability.

The presence of an LLM does not make conventional application vulnerabilities disappear.

This example demonstrates why asking whether a product “tests AI agents” is insufficient. Application teams need to know which of these six surfaces it actually attacks.

Choosing the Right Testing Depth

The six platforms in this list should not be treated as interchangeable alternatives.

The appropriate choice depends on what the team needs to establish.

Many mature organizations will ultimately combine more than one approach. A development team might use lightweight regression tests on every change, run broader agent red teaming before major releases, and continuously penetration-test exposed applications for exploitable weaknesses.

That layered model reflects how application security already works outside AI. SAST did not eliminate DAST. DAST did not eliminate penetration testing. Unit tests did not eliminate integration tests. Agentic AI will not collapse those disciplines into one scanner either. The security architecture
has become more complicated because the application itself has become more capable.

Questions Application Teams Should Ask Before Selecting a Tool

Rather than beginning with the vendor’s vulnerability list, application teams can ask a small number of questions that reveal the actual testing model.

Does the tool test the model or the deployed application?
A model endpoint and a production agent connected to enterprise tools represent very different attack surfaces.

Can attacks adapt?
Determine whether the platform replays predefined prompts or changes its strategy according to the target’s responses.

Does it execute tools?
For agentic applications, tool invocation is often where an AI behavior becomes a security consequence.

Can it test conventional application weaknesses too?
Authentication, authorization, APIs, sessions, and business logic remain relevant even when an LLM controls part of the workflow.

What constitutes a finding?
A failed classifier, suspicious response, reproducible attack, and validated exploit provide different levels of evidence.

What happens after remediation?
A strong testing process should make it possible to rerun the relevant attack and establish whether the fix actually closed the path.

Those questions reveal considerably more than comparing the number of supported attack categories.

FAQs

How is agentic AI testing different from traditional penetration testing?

Traditional penetration testing primarily targets deterministic application, API, infrastructure, and business-logic weaknesses. Agentic applications introduce probabilistic behavior and language-based attack surfaces, including prompt injection and manipulation across multiple interactions.
Effective testing may therefore combine established penetration-testing techniques with AI-specific adversarial methods while examining how model behavior interacts with permissions, tools, data, and application logic.

Is prompt injection testing enough to secure an AI agent?

No. Prompt injection is important, but it represents only one potential failure mechanism. Agents can also expose excessive permissions, insecure tool integrations, authorization flaws, sensitive-data access, unsafe workflow combinations, and conventional application vulnerabilities. Testing
should examine what an attacker can cause the complete system to do, not simply whether the underlying model follows an adversarial instruction.

What is the difference between AI red teaming and AI penetration testing?

AI red teaming broadly explores how an AI system can be manipulated into unsafe or unintended behavior. AI penetration testing places greater emphasis on whether weaknesses can be exploited within a real application and what technical impact results. The categories overlap, and vendors use the
terminology differently, so teams should evaluate the actual testing methodology and evidence produced rather than relying on the label alone.

Should agent security testing run in CI/CD?

Some testing should. Repeatable security regression tests can identify whether model, prompt, tool, or application changes reintroduce known weaknesses. Deeper exploratory testing may require longer-running adversarial assessments or penetration testing. A layered approach allows fast checks to
run frequently while more sophisticated testing searches for attack paths that predefined regression cases do not yet cover.

How should application teams validate an AI security finding?

Validation should establish what occurred, whether the behavior can be reproduced, and what security impact follows. For higher-risk findings, teams should seek evidence that an attacker can cross the relevant security boundary rather than relying only on an evaluator labeling a response unsafe.
After remediation, the original attack should be rerun to confirm that the vulnerability is actually closed.