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.

What MSPs Must Ask Before Adding AI Security to Their Stack

Managed service providers are navigating a moment that did not exist three years ago. The clients you support are deploying AI tools faster than any procurement or security review cycle can keep up with, and they are often doing it without telling you. By the time an MSP learns a client has integrated a third-party AI assistant into their billing workflow or connected an LLM to their internal knowledge base, the exposure is already live.

MSPs Are Now the AI Security Trust Layer

Your clients are not waiting for your guidance before deploying AI. Shadow AI, the term for AI tools adopted by employees without formal IT approval, is now a standard feature of the threat landscape. Research from 2025 consistently shows that a majority of employees at mid-market companies are using AI tools their IT teams have never reviewed. Some of those tools call external APIs. Some process customer data. Some make decisions in automated workflows with no human checkpoint.

That positions MSPs in an unusual place. You inherit the liability for environments you did not fully design, running tools you were not asked to vet, against risks that your traditional monitoring stack was not built to detect. The firms that figure out how to get ahead of this become the trusted AI security partner for their entire client base. The ones that wait for an incident to prompt the conversation find themselves in a much harder position.

Why AI Security Is Fundamentally Different

AI security is not a subset of API security, and it is not just another configuration audit. The risk surface is different in ways that matter for how you scope, staff, and tool a practice around it.

Traditional firewalls and endpoint tools have no visibility into what an AI model sends or receives over HTTPS to an external API.
Prompt injection attacks do not match any existing signature. They look like ordinary user input.
LLM outputs can contain sensitive data exfiltrated in plain language, with no file transfer or protocol anomaly to detect.
Shadow AI is invisible to most SIEM configurations because the traffic originates from legitimate employee devices on legitimate networks.
The attack surface changes every time a model is updated, an integration is added, or a new user gets access to an AI tool.

Questions to Ask Every Client Before You Begin

Before you scope an engagement or recommend a tool, you need a clear picture of the AI footprint across each client environment. These questions are the starting point for that conversation. Some clients will not have answers. That is itself important information.

1Which AI tools are live in production right now, not just approved or in evaluation?
2Were any of those tools added by individual employees or teams without going through IT or security review?
3Do those tools call external APIs, and if so, do you know which ones and under what authentication?
4Who approved the API keys or credentials those tools are using, and when were they last rotated?
5What data do your AI tools have read or write access to? Does that include customer records, financial data, or anything under a compliance scope?
6Are any AI agents or automated workflows making decisions without a human checkpoint?
7Has anyone run a security test against your AI endpoints, distinct from your web application or network scans?
8Do you have logging on what your AI tools are sending to external services and what they are receiving back?
9Are the AI vendors you use under any compliance obligations that affect your own compliance posture (HIPAA, PCI, SOC 2, CMMC)?
10What is your current plan if one of those AI tools starts producing incorrect output, leaks sensitive data, or gets used as an attack vector?

What MSPs Must Ask Before Adding AI Security to Their Stack

MSPs Are Now the AI Security Trust Layer

Why AI Security Is Fundamentally Different

Questions to Ask Every Client Before You Begin

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