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 use a third-party LLM provider — does that change anything?

It makes monitoring more important. Any provider that processes PHI must be covered by a BAA, and you remain responsible for knowing what data flows to them. We help you map those flows and confirm coverage.

What counts as an AI agent here?

Anything that uses a model to take action or generate output with access to your data — chat assistants, charting copilots, intake bots, RAG pipelines, and autonomous tool-using agents all qualify.

Can this support an OCR audit?

Yes. The exported access records, leakage findings, and remediation history are built to serve as evidence of your administrative, physical, and technical safeguards for AI systems.

Healthcare Compliance

HIPAA-Compliant AI Agent Monitoring

AI agents are now booking appointments, summarizing charts, and answering patient questions — often with direct access to PHI. This guide shows healthcare and healthtech teams how to monitor those agents continuously while staying inside HIPAA's Privacy and Security Rules.

The Challenge

An AI agent that can read a patient record can also leak it — in a log, a response, or a misrouted tool call. HIPAA requires you to know where PHI flows, restrict access, and keep an audit trail, but most AI agents are deployed without any of that visibility. The result is an unmonitored system processing your most sensitive data.

HIPAA AI monitoring checklist

What the Security Rule expects you to have in place for AI agents touching PHI

PHI data-flow map

Know exactly which AI agents, LLMs, and APIs can read or write protected health information.

Access controls

Unique, least-privilege credentials for every AI integration with no shared service accounts.

Leakage detection

Monitoring of AI inputs and outputs to catch PHI exposed in responses, prompts, or logs.

Audit controls

An immutable record of AI access to PHI that satisfies the HIPAA audit trail requirement.

Business Associate coverage

Confirmation that every model and AI vendor handling PHI is covered by a signed BAA.

Incident detection

Alerting on anomalous agent behavior so a potential breach is caught in hours, not in a year-end review.

How Chronoloq Helps

See every agent that touches PHI

Discovery surfaces AI agents and model integrations across your environment, including ones IT never approved.

Catch data leakage in AI outputs

Detect PHI exposed through prompts, responses, RAG context, and tool calls before it becomes a reportable breach.

Produce a defensible audit trail

Generate the access and monitoring evidence your compliance team needs to answer an OCR inquiry.

Monitor continuously, not annually

Scheduled re-scans mean your HIPAA posture reflects what's running today, not last year's architecture.

How It Works

Connect Chronoloq to your healthcare applications, APIs, and AI integrations.

We identify every AI agent and LLM with a path to protected health information.

Inputs and outputs are tested for PHI leakage and access-control weaknesses.

You receive findings mapped to HIPAA Security Rule safeguards plus a remediation plan.

Recurring monitoring keeps your audit trail and risk posture current.

Frequently Asked Questions

No. Chronoloq is designed for regulated data and does not require you to expose PHI to third parties. Monitoring focuses on access patterns, configuration, and leakage detection rather than copying patient records.

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.