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 is shadow AI a bigger deal for finance teams?

Finance teams handle the data attackers and regulators care most about: transactions, account details, and PII. When that data flows into ungoverned AI, you risk leakage, fraud exposure, and compliance failures all at once.

Can you detect AI inside tools we didn't build?

Yes. We surface AI-enabled APIs and integrations from your traffic, code, and exposed endpoints, including model calls that originate from third-party tooling.

What do we do once we find it?

Each finding comes with a risk score and recommended action so you can decide quickly whether to shut it down, restrict its access, or formally bring it under governance.

Risk Discovery

Shadow AI Detection for Finance Teams

Your analysts are pasting spreadsheets into chatbots and your engineers are wiring LLMs into internal tools — usually without security's knowledge. This guide helps finance and security teams find that shadow AI, understand the exposure, and bring it under governance.

The Challenge

Shadow AI is the AI nobody approved: a copilot connected to the data warehouse, an LLM summarizing customer accounts, a browser extension reading financial reports. Each one is a path for sensitive financial data to leave your control, and because it's undocumented, it never shows up in a risk review until something goes wrong.

Shadow AI detection checklist

Where ungoverned AI hides inside a finance organization

Unapproved AI integrations

LLM and agent connections wired into internal apps and APIs without a security review.

Data warehouse access

AI tools with read access to financial systems, ledgers, and customer data stores.

Third-party AI in vendor tools

AI features quietly enabled inside the SaaS products your finance team already uses.

Embedded model calls

API endpoints that forward financial data to model providers, discovered from your traffic and code.

Exposed credentials

Model provider API keys leaked in code, configs, or browser tooling.

Governance gaps

AI usage with no owner, no policy, and no monitoring attached to it.

How Chronoloq Helps

See the AI you didn't sanction

Discovery maps AI agents, LLM integrations, and AI-enabled APIs across your environment, not just the approved ones.

Understand the financial-data exposure

Each finding shows what financial data the tool can reach and how it could leak.

Prioritize what to shut down or govern

Risk scoring tells your team which shadow AI needs immediate action versus a policy fix.

Bring AI under continuous oversight

Recurring scans catch the next unapproved tool before it becomes the next incident.

How It Works

Point Chronoloq at your domains, internal apps, and API gateways.

We discover AI agents, LLM integrations, and AI-enabled endpoints in use.

Each is classified by the financial data it can access and its risk level.

You get a ranked list of shadow AI to govern, remediate, or remove.

Scheduled scans keep finding new shadow AI as your teams adopt more tools.

Frequently Asked Questions

Any AI tool, agent, or model integration used inside your organization without security's approval or oversight — from a chatbot connected to internal data to an AI feature switched on inside a SaaS product.

API Discovery Tool for Compliance Teams

OWASP LLM Top 10 Checklist for Fintech

See where you actually stand

Get a free AI Security Audit and discover every AI agent, LLM, and API in your environment — with a prioritized plan to close the gaps.