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.

We're pre-Series A — is this overkill?

No. Most fintech startups need SOC 2 to close enterprise deals, and AI is increasingly the area auditors probe first. Getting your AI inventory and controls right early is far cheaper than remediating exceptions during the audit.

Do you need access to production to do this?

No production access is required to get started. We can work from API documentation, integration metadata, and read-only scanning. Deeper testing can be scoped with your team.

How is this different from a generic security scanner?

Generic scanners don't understand AI-specific risks like prompt injection or model data leakage, and they don't map findings to SOC 2 controls. Chronoloq does both, so the output is audit evidence rather than raw vulnerabilities.

Compliance Guide

SOC 2 AI Compliance for Fintech Startups

Auditors now expect your AI systems to fall under the same SOC 2 controls as the rest of your stack. This guide shows fast-moving fintech teams how to inventory their AI footprint, map it to the Trust Services Criteria, and produce audit-ready evidence without slowing down shipping.

The Challenge

Fintech startups adopt LLMs and AI agents faster than their compliance program can keep up. Models touch transaction data, KYC documents, and customer PII, but they rarely appear in the SOC 2 system description. When the auditor asks 'show me every system that processes customer data,' the AI layer is the gap that turns a clean report into a list of exceptions.

Your SOC 2 AI readiness checklist

The controls auditors actually ask fintech teams to evidence for AI systems

Complete AI & API inventory

Every LLM, agent, vector store, and API that touches customer or financial data, mapped to a data-flow diagram.

Access controls (CC6)

Least-privilege keys for model providers, logged and rotated, with no shared production credentials.

Change management (CC8)

Prompt, model, and guardrail changes tracked through review and approval like any other production change.

Monitoring (CC7)

Logging and alerting on AI inputs/outputs so anomalous behavior and data leakage are detectable.

Vendor management (CC9)

Documented risk review of every model and AI tooling vendor in your subprocessor list.

Evidence collection

Repeatable exports — scan reports, risk scores, remediation history — you can hand straight to the auditor.

How Chronoloq Helps

Find the AI you forgot to document

Automated discovery surfaces shadow AI agents and undocumented model integrations so nothing is missing from your system description.

Map findings to Trust Services Criteria

Each vulnerability is tied to the relevant SOC 2 control, turning a scan into audit evidence instead of a to-do list.

Close gaps before the auditor sees them

A prioritized remediation plan focuses your small team on the issues that actually create exceptions.

Stay continuously audit-ready

Re-scan on a schedule so your evidence reflects reality at the moment the audit window opens.

How It Works

Point Chronoloq at your domains, API gateways, and AI integrations.

We discover and classify every AI agent, LLM, and API that handles regulated data.

Findings are scored and mapped to the relevant SOC 2 Trust Services Criteria.

You receive a prioritized remediation plan and exportable evidence package.

Schedule recurring scans so your SOC 2 evidence stays current between audits.

Frequently Asked Questions

Yes. SOC 2 is scoped to any system that processes customer data within your defined boundary. If an LLM or AI agent touches that data, it falls under the same Trust Services Criteria as the rest of your stack and the auditor will expect controls and evidence for it.

Related Guides

Shadow AI Detection for Finance Teams

API Discovery Tool for Compliance Teams

OWASP LLM Top 10 Checklist for Fintech

See where you actually stand

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