What are agentic pentesting tools?

Traditional security testing tools excel at applying proven security checks quickly and consistently. Agentic pentesting tools add another dimension: they use AI agents to explore applications, adapt their testing based on runtime behavior, and investigate attack paths that may require multiple steps or changing strategies.

For AppSec teams, this doesn’t make established dynamic application security testing (DAST) obsolete. It creates an opportunity to combine the broad, repeatable coverage of DAST with deeper, application-specific investigation that would otherwise require more manual effort.

What is an agentic pentesting tool?

An agentic pentesting tool uses autonomous or semi-autonomous AI agents to plan and perform security testing, make decisions based on what they encounter, and adjust their approach as an assessment progresses.

Automation itself is nothing new in application security. DAST has been automating runtime security testing for decades, applying security checks across applications and APIs at a scale that would be impractical to achieve manually.

What makes agentic security testing different is how the automation operates.

A conventional automated test generally starts with predefined knowledge about a vulnerability class and applies security checks designed to identify it. An agentic system can also work toward broader objectives. It might observe an application’s behavior, form a hypothesis about a potential weakness, select the next action based on that hypothesis, evaluate the result, and change its strategy accordingly.

This makes agentic testing particularly interesting for areas where security depends on context rather than a single vulnerable request.

The goal is not to give an AI model unrestricted freedom to hack an application – that’s not an option for governed application security. Effective agentic testing still needs defined scope, controls, reliable runtime information, and rigorous validation. The difference is that AI can make more of the decisions between individual testing steps instead of relying exclusively on a predetermined sequence.

How are agentic pentesting tools different from traditional security scanners?

Traditional DAST provides systematic, repeatable runtime testing at scale. Agentic pentesting adds adaptive reasoning that can pursue application-specific objectives and investigate multi-step attack paths. Here’s how they compare:

Traditional DAST	Agentic pentesting
Applies established security checks systematically	Adapts testing based on observations and objectives
Optimized for broad, repeatable runtime coverage	Optimized for deeper application-specific investigation
Uses established detection and validation logic	Can reason over observations and generate new testing actions
Can maintain authentication and application context	Can use context to modify a multi-step testing strategy
Designed for continuous automated scanning	Adds goal-oriented exploration to automated testing

The reason that distinction matters is because modern DAST is already far more capable than a simple collection of stateless requests. Products such as Invicti Web + API (formerly Acunetix) can crawl modern applications, maintain authentication, test APIs, execute multi-step workflows, and analyze state across API requests.

Agentic pentesting builds on those capabilities rather than replacing them.

A useful way to think about the relationship is coverage and depth. DAST can repeatedly test large numbers of applications and APIs for known vulnerability classes. Agentic testing can then spend additional effort exploring areas where finding a vulnerability requires reasoning about how several requests, roles, resources, or application states relate to one another.

Why do modern applications need more adaptive security testing?

Web applications increasingly expose functionality through APIs, JavaScript-heavy frontends, distributed services, and authenticated workflows. Testing them thoroughly requires understanding more than isolated pages and parameters.

Consider an authorization flaw. A security test might need to create or identify a resource as one user, capture its identifier, switch to a different security context, attempt an operation against that resource, and determine whether the resulting behavior violates the intended authorization model.

Likewise, a business logic vulnerability might only appear after a specific sequence of otherwise legitimate operations. Each individual request could be valid while the overall workflow produces an unintended security outcome.

Modern DAST has developed increasingly sophisticated mechanisms for authenticated scanning, API testing, stateful workflows, and other application-aware testing. Agentic pentesting pushes further by allowing an AI system to decide dynamically what to investigate next rather than requiring every possible sequence to be anticipated in advance.

This becomes increasingly useful as application architectures grow more dynamic and the volume of software increases. AI-assisted development can accelerate code production, but faster code generation also means more functionality for security teams to discover, test, and retest.

How do agentic pentesting tools work?

Agentic pentesting tools combine runtime observations with AI-driven decision-making to plan tests, execute actions, evaluate results, and adapt their testing strategy.

Implementations differ, but an agentic testing process can involve several stages.

First comes reconnaissance. The testing system needs to understand what it is assessing, including reachable application functionality, technologies, input points, APIs, authentication boundaries, and other useful context.

Next, the agent or agents form testing objectives. Instead of simply running every available test in a fixed sequence, an agentic system can use the information gathered so far to determine which attack hypotheses are worth investigating.

The system then interacts with the running application and evaluates the results. If an action reveals new information, the agent can use that information when deciding what to do next. This feedback loop is what enables adaptive exploration.

Multiple agents can also specialize in different areas and share context. One might investigate authentication behavior while another concentrates on injection vulnerabilities or application workflows. Their observations can influence subsequent testing decisions.

Finally, suspected vulnerabilities need validation. This step is especially important when AI is involved. Large language models can generate plausible hypotheses, but a plausible security issue is not necessarily a real vulnerability. Agentic testing needs runtime evidence to distinguish an interesting idea from an exploitable finding.

What vulnerabilities are agentic pentesting tools best suited to finding?

Agentic pentesting is particularly well suited to investigating vulnerabilities that depend on context, authorization, application state, or multiple related actions.

Potential examples include:

• Broken object-level authorization (BOLA), also commonly called insecure direct object reference (IDOR), where access to an object depends on relationships between users, identifiers, and permissions.
• Broken function-level authorization (BFLA), where a user can invoke functionality that should be restricted to another role.
• Business logic flaws that abuse legitimate application functionality in unintended sequences.
• Authentication and session weaknesses that require several actions or security contexts to investigate.
• Privilege escalation paths where testing needs to explore relationships between roles, permissions, and application functionality.
• Chained vulnerabilities where one finding provides the information or access needed to pursue another.

Agentic technology doesn’t guarantee discovery of these issues. Business logic in particular can depend on organizational rules or intent that no testing tool can infer reliably from application behavior alone.

What agentic testing can do is expand the range of workflows that automated testing can investigate by making testing decisions dynamically rather than relying entirely on predefined paths.

Why are APIs a major driver behind agentic security testing?

APIs expose application data and business operations directly, making authentication, authorization, state, and workflow relationships central to API security. REST, GraphQL, SOAP, and other APIs can also create a large and rapidly changing attack surface. Some endpoints are formally documented, while others may be outdated, undocumented, or simply unknown to the security team.

Discovery therefore remains a prerequisite for effective testing. You cannot systematically test an API endpoint until you know it exists. This is an area where scalable DAST and agentic testing are complementary. Invicti Web + API combines API discovery with runtime testing of APIs, including authenticated and stateful testing.

Agentic techniques can build on that visibility to investigate relationships between endpoints, identities, resources, and workflows in greater depth. For example, an agent could potentially use information returned by one endpoint to inform how it tests another, or modify its investigation after observing unexpected authorization behavior. That ability to adapt is valuable when the interesting security property exists across a workflow rather than inside one request.

Where does agentic pentesting fit into an AppSec program?

Agentic pentesting makes the most sense as an additional layer of runtime testing, not as a replacement for the scalable testing practices teams already rely on.

API security testing

APIs are a natural use case because security frequently depends on relationships between endpoints, resources, identities, and permissions. Agentic testing can support deeper investigation of authorization behavior, authentication flows, and sequences of API operations, while DAST provides repeatable vulnerability coverage across the broader API surface.

Business logic testing

Business logic flaws rarely fit neatly into a generic vulnerability signature. An agentic system can explore workflows, evaluate responses, and change its actions as it learns more about how an application behaves. Human oversight remains important where determining whether behavior is actually insecure requires knowledge of business intent.

Continuous application security testing

DAST remains the practical foundation for frequent runtime testing in development pipelines and production environments. Agentic capabilities can add deeper investigation where needed without turning every routine security check into a manual pentest.

This division of labor matters. Well-understood vulnerability classes don’t necessarily need expensive AI reasoning when deterministic security checks can test them efficiently.

Complex authentication and authorization

Applications may combine OAuth 2.0, JSON Web Tokens (JWTs), single sign-on (SSO), multiple user roles, session state, and other controls. Testing these environments requires maintaining the right security context throughout an assessment. Agentic systems can use that context to guide subsequent testing and investigate behavior across multiple identities or application states.

Shadow and undocumented APIs

API discovery helps identify endpoints outside the security team’s known inventory. Once those endpoints are identified, DAST can provide systematic security testing, while adaptive testing can investigate deeper relationships and workflows where appropriate.

How does Invicti apply agentic testing to penetration testing?

Invicti Agentic Pentest combines AI-driven reasoning with Invicti’s established DAST and runtime validation capabilities, allowing adaptive testing to build on a proven dynamic testing foundation.

The assessment starts from runtime knowledge of the application rather than asking an AI model to reason in isolation. Invicti’s existing crawling and DAST capabilities help establish attack points and application context. AI agents can then coordinate testing, share information, adapt their approaches, and create application-specific security checks as they investigate.

The validation stage is equally important.

AI can increase the number and variety of hypotheses a testing system can generate. Without corresponding validation, that could simply create a new source of security noise. Invicti addresses this by applying its established runtime confirmation techniques to candidate findings before reporting them as confirmed vulnerabilities.

That builds on the same principle behind Invicti’s proof-based scanning: where a vulnerability can be safely confirmed, testing should provide evidence that the issue is exploitable rather than leaving security and development teams to determine whether a scanner’s suspicion is real.

The result combines automation with adaptive testing behavior: DAST handles work that benefits from fast, established security checks, while AI agents can focus on areas where additional reasoning and application-specific exploration add value.

How does agentic pentesting complement DAST?

DAST provides broad, repeatable runtime security testing at scale. Agentic pentesting adds investigative depth when testing requires adaptive decisions across application states, workflows, or multiple actions.

This makes the technologies complementary.

Invicti Web + API provides a DAST foundation for continuously discovering and testing web applications and APIs, validating supported vulnerabilities, integrating testing into development workflows, and retesting as applications change.

Agentic Pentest adds a deeper assessment layer for cases where predefined testing alone may not provide sufficient investigative depth.

There is no reason to spend AI resources rediscovering every vulnerability that an established DAST check can identify efficiently. Equally, there are security questions where predefined checks alone cannot anticipate every relevant sequence or application-specific condition.

Combining the two allows each approach to do the work it is best suited to handle.

What are the limitations of agentic pentesting tools?

Agentic pentesting introduces new testing possibilities, but autonomy does not remove the fundamental limitations of automated security testing.

The first limitation is business context. A system can observe what an application allows, but it may not know what the organization intended to allow. An unusual transaction sequence could represent a critical business logic flaw or perfectly legitimate behavior.

The second is validation. Generative AI is designed to produce plausible outputs, which makes unverified AI-generated security findings particularly problematic. Any agentic testing approach should make a clear distinction between hypotheses, candidate findings, and vulnerabilities supported by runtime evidence.

Scope and governance also matter. Autonomous security testing interacts actively with running systems, so organizations need appropriate controls over targets, credentials, actions, and environments.

Finally, agentic testing cannot guarantee complete vulnerability coverage – and neither can DAST, SAST, manual pentesting, or any other single testing technique. A mature AppSec program combines complementary methods and applies them where they provide useful evidence about real application risk.

Combining DAST and agentic pentesting with a hybrid approach

Agentic pentesting expands what automated runtime testing can investigate, especially when vulnerabilities depend on workflows, authorization relationships, and multi-step application behavior.

The strongest foundation remains comprehensive, repeatable testing. DAST can provide that coverage continuously across running applications and APIs, while agentic testing adds adaptive investigation where additional depth is valuable.

For teams getting started or looking to automate more of their everyday application and API security testing, Invicti Web + API provides scalable DAST with runtime validation, authenticated testing, API coverage, and integration into development workflows.

When deeper investigation is required, Invicti Agentic Pentest adds AI-driven reasoning and coordinated adaptive testing on top of Invicti’s runtime security foundation.

See how Invicti Agentic Pentest combines adaptive AI-driven testing with Invicti’s proof-based DAST foundation – or request a demo to see it in action in your application environments.

Frequently asked questions about agentic pentesting tools