Most application security teams now own at least one LLM-backed feature — a support chatbot, a RAG search layer over internal documents, an agent that files tickets or drafts emails. The uncomfortable question is who is actually testing it. Static analysis sees the orchestration code but not the model’s behavior. Traditional DAST scanners were built for deterministic request/response applications and largely ignore the conversational surface. And manual red-teaming of LLM features is expensive, slow, and hard to repeat every release.
This guide covers what dynamic testing of LLM-backed applications actually involves, which OWASP Top 10 for LLM Applications risks are dynamically testable, and where Offensive360’s DAST and authorization-gated AI Pentester fit compared with generic scanners and manual engagements.
Why LLM features break classic DAST assumptions
A conventional DAST scanner works because web applications are mostly deterministic: send a payload, observe the response, match a signature. Three properties of LLM-backed features undermine that model:
- Non-determinism. The same prompt can produce different outputs across requests. A vulnerability that manifests once in five attempts is still a vulnerability, but a single-shot scanner will miss it four times out of five. Testing needs repetition, variation, and semantic evaluation of responses — not string matching.
- The vulnerability lives in behavior, not in a response code. A prompt injection that convinces a support bot to reveal its system prompt returns HTTP 200. Nothing in the transport layer looks wrong. The finding exists in what was said, which means the scanner must understand the application’s intended boundaries and detect when the model crosses them.
- The blast radius is defined by connected tools, not the model. A chatbot that can only chat has a limited worst case. An LLM agent with access to a database, an email API, or internal services turns prompt injection into server-side request forgery, data exfiltration, or unauthorized actions. Dynamic testing has to map what the model can do, not just what it can say.
There is a fourth, organizational problem: LLM features change constantly. Prompt tweaks, model version bumps, new tools added to an agent — each one can reopen a previously fixed issue. That argues for automated, repeatable dynamic testing wired into the release cycle, with manual red-teaming reserved for depth.
Which OWASP LLM Top 10 risks are dynamically testable
The OWASP Top 10 for LLM Applications (2025) is the reference taxonomy. Not all ten categories are equally suited to dynamic testing — some are supply-chain or training-time concerns — but the majority of exploitable production risk is reachable from the outside:
- LLM01: Prompt Injection. The core dynamic test. Direct injection through user input, and indirect injection through content the application retrieves — documents in a RAG corpus, web pages, email bodies. Indirect injection is where generic testing usually stops and where real attacks increasingly start.
- LLM02: Sensitive Information Disclosure. Probing whether the model leaks PII, credentials, internal data from its context window, or content from other users’ sessions.
- LLM05: Improper Output Handling. The classic bridge between AI risk and web risk: model output that flows unsanitized into HTML (XSS), SQL, shell commands, or downstream APIs. This is testable with the same rigor as traditional injection — and it is where LLM findings become conventional, high-severity findings.
- LLM06: Excessive Agency. Enumerating what tools and actions the model can invoke, then testing whether adversarial input can trigger actions the user should not be able to reach.
- LLM07: System Prompt Leakage. Extraction attempts against the system prompt, which frequently contains business logic, internal endpoints, or credentials that should never have been there.
- LLM10: Unbounded Consumption. Resource exhaustion and cost amplification — testable in principle, but in production environments it must be handled carefully or explicitly excluded (see the note on denial-of-service safety below).
Categories such as LLM03 (Supply Chain) and LLM04 (Data and Model Poisoning) are better addressed through composition analysis and ML pipeline controls than through black-box dynamic testing; a credible vendor should say so rather than claim ten-out-of-ten coverage from a scanner.
How Offensive360 approaches LLM application testing
Offensive360’s DAST tests LLM-backed applications against the OWASP Top 10 for LLM Applications (2025) as part of the same crawl-based engine that tests the rest of the application — the chatbot endpoint, the APIs behind it, and the conventional web attack surface are one target, not three tools. Every finding ships with request/response evidence, so a reported prompt injection is a reproducible transcript, not a probabilistic claim.
For depth beyond scanning, the platform includes the AI Pentester: AI-driven, authorized penetration-testing engagements that follow the PTES methodology through reconnaissance, vulnerability analysis (launching and correlating a real DAST scan), exploitation, and reporting. Its design center is control — which is precisely the property that matters when the target is a production system with an LLM attached:
- Signed authorization gate. No engagement runs without an immutable authorization record: a named signatory, a typed signature that must exactly match, eight explicit attestations, and the signer’s IP and timestamp. A missing attestation or signature mismatch blocks the launch server-side. Testing production AI features without documented authorization is how security teams end up explaining themselves to legal; the gate makes the paperwork a precondition, not an afterthought.
- Human approval before exploitation. The engine pauses at “awaiting approval” and will not move from identifying an issue to exploiting it until a human explicitly approves. For LLM agents wired into real systems, this is the difference between demonstrating that an injection could send email and actually sending it.
- Visible kill switch. Any running engagement can be stopped instantly by the operator.
- Denial-of-service force-disabled. DoS techniques cannot be enabled — a hard guarantee, which matters doubly for LLM applications where “testing” unbounded consumption against a metered model API can produce a very real bill or an outage.
- On-premise and air-gapped capable. The engagement runs from your infrastructure via the Offensive360 OVA; prompts, transcripts, and findings never leave your network. For teams testing internal LLM features over sensitive data, this is often a hard requirement rather than a preference.
Findings map to OWASP WSTG and MITRE ATT&CK with prioritization and remediation guidance, so LLM-specific results land in the same reporting frame your program already uses.
Comparison: options for testing LLM-backed applications
| Criterion | Generic web DAST | LLM red-team point tools | Manual red team engagement | Offensive360 DAST + AI Pentester |
|---|---|---|---|---|
| Conventional web/API coverage | Yes | Typically no | Yes | Yes — same engine, same scan |
| OWASP LLM Top 10 dynamic coverage | Largely absent | Focused on prompt attacks; depth varies by tool | Depends on team expertise | Dynamically testable categories, with evidence |
| Indirect prompt injection (RAG/retrieved content) | No | Varies — verify per tool | Yes, if scoped | Yes, within crawled/authorized scope |
| Evidence per finding | Request/response for classic vulns | Varies | Report narrative | Request/response transcript for every finding |
| Authorization/consent controls | Scan-level config | Varies | Contract and RoE | Signed in-product authorization record, enforced server-side |
| Human approval before exploitation | N/A | Varies | Inherent | Built-in gate; safe mode supported |
| DoS safety guarantee | Configurable | Varies | Contractual | Force-disabled, cannot be enabled |
| Air-gapped / on-prem operation | Some tools | Mostly cloud — verify | Yes | Yes (OVA) |
| Repeatable per release | Yes | Partially | No — periodic | Yes |
| Cost model | License | License/usage | Per engagement | Flat platform pricing, unlimited scans |
Characterizations of other tool categories are general; individual products vary, and buyers should verify LLM-specific coverage claims against each vendor’s documentation.
When Offensive360 is not the right choice
- You are testing the model itself, not an application. If your goal is evaluating a foundation model’s safety alignment, jailbreak robustness across model versions, or training-data extraction at the research level, dedicated model-evaluation frameworks and research tooling are the right instruments. Our platform tests applications that embed models.
- Your LLM feature is a thin wrapper with no data access and no tools. If a marketing-site chatbot has no retrieval, no tools, and no sensitive context, a periodic manual review may be proportionate. Buy tooling when the feature’s blast radius justifies it.
- You need ML pipeline and training-time security. Data poisoning, model provenance, and MLOps supply-chain controls (LLM03/LLM04) need pipeline-level controls; a dynamic scanner is the wrong layer, whoever sells it to you.
- You want a fully autonomous attack platform. Some buyers explicitly want maximum autonomy with minimal human gating. Our AI Pentester is deliberately built the other way — authorization, approval gates, and a kill switch — and if unsupervised autonomy is the requirement, we are the wrong vendor by design.
Frequently asked questions
Can prompt injection really be tested automatically, given non-determinism? Yes, with the right method: repeated and varied adversarial inputs, semantic evaluation of responses rather than string matching, and reproducible transcripts as evidence. What automation buys you is coverage and repeatability every release; what it does not replace is creative, contextual attack design — which is why the AI Pentester pairs automated testing with human-approved exploitation rather than pretending one substitutes for the other.
Is it safe to run this against a production LLM application? The engagement model is built for that case: documented signed authorization before anything runs, scope and rules of engagement enforced throughout, human approval before exploitation, an instant kill switch, and denial-of-service techniques disabled at the engine level with no override. That said, we would still recommend testing against staging first where one exists.
How do you test LLM features in an air-gapped or on-premise environment? The full platform, including the AI Pentester, deploys via the Offensive360 OVA inside your network. Engagement records, prompts, and findings stay on your infrastructure. This also covers internally hosted models: the test targets your application’s endpoints, wherever the model runs.
Our chatbot only answers from a knowledge base. Is indirect prompt injection still a risk? That architecture is precisely where indirect injection lives. If an attacker can influence any document the retrieval layer can surface — a public web page, a submitted form, an emailed attachment that gets indexed — they can plant instructions the model may follow. Testing has to exercise the retrieval path, not just the chat input box.
Does the OWASP LLM Top 10 replace the classic OWASP Top 10 for these apps? No — it extends it. An LLM application still has authentication, access control, APIs, and injection surface, and improper output handling (LLM05) explicitly connects model output back to classic vulnerability classes. That is the practical argument for testing the whole application with one engine rather than bolting an LLM point tool onto a separate web scanner.
Next steps
- Book a demo — bring one of your LLM-backed features and we will walk through how an authorized engagement against it would be scoped, gated, and reported.
- Run a free scan — start with your application’s conventional attack surface today and see the evidence quality for yourself.