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.

How is this different from a security questionnaire?

Questionnaires rely on the vendor's self-reported answers. Chronoloq tests what's actually exposed, so you get evidence rather than assertions — including AI-specific risks questionnaires never ask about.

What if the vendor uses a major model provider?

Using a reputable provider doesn't remove your risk. How the vendor integrates, authenticates, and constrains that model determines whether PHI is safe. We assess the integration, not just the underlying model.

Does this help with our own compliance?

Yes. A documented, repeatable AI vendor risk process is itself an expectation under HIPAA and SOC 2 vendor-management controls, and the scorecards serve as evidence.

Vendor Risk

AI Vendor Risk Assessment for Healthtech

Every AI vendor you integrate inherits access to your patients' data and your compliance obligations. This guide gives healthtech teams a structured way to assess AI vendors — for model security, PHI exposure, and contractual coverage — before they become your weakest link.

The Challenge

Healthtech runs on third-party AI: transcription services, diagnostic models, intake assistants, and RAG tools built on someone else's stack. A single insecure vendor can expose PHI, fail an audit, or introduce an OWASP LLM vulnerability you never tested. Standard vendor questionnaires don't catch any of it because they were written before AI.

AI vendor risk assessment checklist

What to evaluate before you trust a vendor with patient data

PHI data flows

Exactly what patient data the vendor's AI receives, stores, and uses for training.

Model security

Resistance to prompt injection, jailbreaks, and data leakage in the vendor's AI features.

BAA and contracts

A signed Business Associate Agreement and clear data-handling terms covering AI processing.

Access and authentication

How the vendor's API authenticates and whether it follows least-privilege access.

OWASP LLM coverage

Evidence the vendor has tested against the OWASP LLM Top 10 risks.

Incident and breach terms

Defined notification timelines and responsibilities if the vendor is compromised.

How Chronoloq Helps

Test vendors, don't just survey them

Move beyond questionnaires with evidence-based scanning of the vendor's exposed AI and APIs.

Quantify PHI exposure

Understand precisely what patient data a vendor can reach and how it could leak.

Standardize every assessment

A repeatable scorecard so every AI vendor is judged against the same healthtech bar.

Re-assess as vendors change

Vendors ship new AI features constantly — scheduled re-assessment keeps your risk view current.

How It Works

Share the vendor's domains, API documentation, or integration details.

Chronoloq scans the vendor's exposed AI agents, LLMs, and APIs for risk.

Findings are scored for PHI exposure, model security, and OWASP LLM gaps.

You receive a vendor risk scorecard and questions to raise before signing.

Re-run the assessment whenever the vendor changes its AI footprint.

Frequently Asked Questions

Yes. We can evaluate a vendor's externally exposed AI and APIs from public documentation and reachable endpoints, giving you risk insight before you sign or share data.

Related Guides

HIPAA-Compliant AI Agent Monitoring

API Discovery Tool for Compliance Teams

OWASP LLM Top 10 Checklist for Healthcare

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.