How do I test my LLM or AI chatbot for prompt injection?

To test an LLM for prompt injection, point a dedicated AI security scanner at your endpoint and run a battery of adversarial prompts covering direct injection, indirect injection via retrieved documents, and jailbreak attempts. Chronoloq automates this process: you provide the LLM endpoint URL and optional auth headers, and the scanner runs 39+ test cases mapped to OWASP LLM Top 10, returning a grade and per-finding evidence. Manual testing can supplement automated scans by crafting context-specific payloads for your application's data flows.

What tools detect OWASP LLM Top 10 vulnerabilities?

Tools that cover the OWASP Top 10 for LLM Applications (2025 edition) include Chronoloq, Garak (open-source, NVIDIA), PromptFoo (open-source), and LLM Guard (runtime). Chronoloq is the only platform that covers the full OWASP LLM Top 10 alongside the OWASP API Security Top 10 in a single product, which matters when your attack surface includes both AI endpoints and traditional APIs.

How do I find shadow APIs and undocumented endpoints in my organization?

Shadow API discovery requires active crawling of your domains and subdomains combined with traffic analysis. Chronoloq's API Discovery module scans your domains, enumerates subdomains via certificate transparency logs and DNS records, probes common API paths, and classifies each discovered endpoint by authentication state and technology. This surfaces APIs that were never documented, left over from decommissioned services, or introduced through third-party integrations without your security team's knowledge.

What is the difference between LLM security testing and API security testing?

LLM security testing focuses on AI-specific attack classes: prompt injection, jailbreaks, data leakage through model outputs, model extraction, RAG poisoning, and agentic risks like tool abuse and privilege escalation. API security testing covers the OWASP API Top 10: broken object-level authorization (BOLA/IDOR), broken authentication, excessive data exposure, and injection flaws in traditional HTTP endpoints. Modern AI applications need both because LLM endpoints are also APIs and can be exploited through both attack surfaces simultaneously.

How do I secure a RAG pipeline against data leakage and poisoning?

Securing a RAG (Retrieval-Augmented Generation) pipeline requires addressing three layers: the retrieval mechanism (prevent unauthorized document access and index poisoning), the prompt construction (prevent injected instructions in retrieved content from hijacking the LLM), and the output (screen for PII and confidential data before returning responses). Chronoloq tests RAG pipelines by probing the retrieval endpoint directly, injecting adversarial documents into test queries, and checking whether retrieved content can steer the model's behavior outside its intended scope.

What is MCP security and how do I test MCP servers for vulnerabilities?

Model Context Protocol (MCP) security covers vulnerabilities specific to MCP server implementations: tool poisoning (malicious tool descriptions that manipulate agent behavior), rug pulls (tool behavior changing after approval), privilege escalation through tool calls, and RCE vulnerabilities in MCP SDK implementations. To test an MCP server, scan the tool manifest for malicious or overly permissive descriptions, probe each exposed tool for injection and boundary violations, and verify that the server enforces least-privilege access. Chronoloq includes MCP security testing as part of its AI scanner.

How much does AI security testing cost for a small or mid-size company?

AI security testing costs vary widely by approach. Enterprise penetration testing firms charge $15,000 to $50,000 per engagement with infrequent cadence. Open-source tools like Garak are free but require significant engineering time to operate and interpret. Chronoloq starts at $299 per month for continuous automated AI and API security scanning, which gives small and mid-size organizations recurring coverage at a fraction of the cost of periodic manual engagements.

What does OWASP LLM Top 10 compliance require for my AI application?

OWASP LLM Top 10 (2025 edition) compliance means addressing: LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, LLM03 Supply Chain Vulnerabilities, LLM04 Data and Model Poisoning, LLM05 Improper Output Handling, LLM06 Excessive Agency, LLM07 System Prompt Leakage, LLM08 Vector and Embedding Weaknesses, LLM09 Misinformation, and LLM10 Unbounded Consumption. Practically this requires testing your LLM endpoints with adversarial inputs, auditing your RAG pipeline, reviewing tool and agent permissions, and establishing output filtering before user-facing deployment.

How do I detect and monitor AI agents running in my organization without my knowledge?

Shadow AI agents can be detected through a combination of DNS and subdomain enumeration, traffic pattern analysis, API gateway logs, and active discovery scans. Chronoloq's AI discovery module identifies AI-classified endpoints across your domains by probing for LLM-specific request/response signatures, OpenAI-compatible API patterns, and agentic workflow indicators. This surfaces both sanctioned AI integrations and unauthorized deployments introduced by individual teams or third-party vendors.

Why a fintech-specific checklist?

Because the same risk has very different consequences depending on your data. In fintech, your AI handles transaction and customer financial data, so we prioritize and frame each OWASP risk around that exposure.

Can you actually test all ten automatically?

Yes. Chronoloq generates and runs evidence-based tests across every OWASP LLM category and reports a pass/fail result with proof for each, rather than just a generic checklist.

How often should we re-check?

Every time you change prompts, models, tools, or data sources. Scheduled re-scans keep your OWASP LLM coverage current as your AI evolves.

OWASP LLM Top 10

OWASP LLM Top 10 Checklist for Fintech

The OWASP LLM Top 10 is the standard for large language model risk — but the stakes change by industry. This checklist applies all ten risks to fintech, where your AI handles transaction and customer financial data, and shows how to test each one with real evidence.

The Challenge

AI assistants and agents in fintech handle money movement, KYC, and account data, making them a direct target for manipulation and data theft. Generic LLM advice doesn't account for the data and regulations specific to fintech. Working through the OWASP LLM Top 10 in your own context is the difference between a checklist you read and one you can prove you've met.

The OWASP LLM Top 10 for Fintech

Each risk applied to systems handling transaction and customer financial data

LLM01 Prompt Injection

Confirm both direct and indirect injection can't override your system instructions or hijack agent behavior.

LLM02 Sensitive Information Disclosure

Verify the model can't leak PII, secrets, or other confidential data through its responses.

LLM03 Supply Chain

Check the models, plugins, and datasets you depend on for compromised or untrusted components.

LLM04 Data & Model Poisoning

Ensure training data, fine-tunes, and RAG sources can't be tampered with to corrupt outputs.

LLM05 Improper Output Handling

Confirm model output is treated as untrusted and can't trigger injection downstream.

LLM06 Excessive Agency

Limit what agents and tools can do so a manipulated model can't take destructive actions.

LLM07 System Prompt Leakage

Test that hidden prompts, rules, and configuration can't be extracted.

LLM08 Vector & Embedding Weaknesses

Secure RAG pipelines and vector stores against injection and unauthorized retrieval.

LLM09 Misinformation

Guard against confidently wrong or fabricated output reaching users unchecked.

LLM10 Unbounded Consumption

Protect against denial-of-wallet and resource-exhaustion attacks on your AI endpoints.

How Chronoloq Helps

Test every risk, not just prompt injection

Automated probing covers all ten OWASP LLM categories so nothing on the list is left untested.

Framed for fintech

Findings are prioritized by their impact on transaction and customer financial data, not generic severity.

Evidence for every finding

Each result includes the exact prompt and response that proved the risk, so fixes are clear.

A defensible coverage record

Show auditors and customers you've tested against the recognized OWASP LLM standard.

How It Works

Point Chronoloq at your AI endpoint, chatbot, or agent.

We run tests for all ten OWASP LLM risks against your fintech systems.

Successful exploits are captured with prompt-and-response evidence.

You receive a per-category checklist of pass/fail results and remediation steps.

Schedule recurring scans so every model change is re-checked against the Top 10.

Frequently Asked Questions

It's the OWASP project's list of the ten most critical security risks for large language model applications — covering prompt injection, data disclosure, supply chain, excessive agency, and more. It's the recognized baseline for LLM security.

Related Guides

LLM Red Teaming for Small Teams

Shadow AI Detection for Finance Teams

API Discovery Tool for Compliance Teams

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