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.

Is automated red teaming as good as a human?

It's not a full replacement for an expert engagement, but it gives you continuous, repeatable coverage of the most common and damaging LLM attacks — far more than most small teams get from occasional manual testing.

Will testing harm our production AI?

Testing is designed to be safe and non-destructive. You can scope it to staging or constrain it for production so you get coverage without disrupting users.

What standard does it follow?

Findings are mapped to the OWASP LLM Top 10, the recognized framework for large language model risks, so your coverage is measurable and defensible.

Offensive Testing

LLM Red Teaming for Small Teams

Red teaming used to require a specialist crew you don't have. This guide shows lean teams how to run continuous, automated LLM red teaming — probing for prompt injection, jailbreaks, and data leakage — and get evidence-backed fixes without hiring a security department.

The Challenge

Small teams ship AI features fast but have no one whose job is to attack them. Manual red teaming is expensive and point-in-time, so most LLM apps go to production never having been tested against the attacks that actually matter: prompt injection, jailbreaks, system-prompt leakage, and unsafe tool use.

LLM red teaming checklist

The attacks every AI feature should be tested against before launch

Direct prompt injection

Attempts to override your system prompt and hijack the model's behavior.

Indirect prompt injection

Malicious instructions hidden in retrieved documents, web content, or tool output.

Jailbreaks

Techniques that bypass safety guardrails to produce restricted or harmful output.

System prompt leakage

Probes that try to extract your hidden instructions and configuration.

Sensitive data leakage

Tests for PII and confidential data exposed through model responses.

Unsafe tool & agent actions

Checks that agents can't be tricked into executing unintended or destructive actions.

How Chronoloq Helps

Red team without the headcount

Automated attack generation runs the probes a specialist would, so a small team gets coverage it couldn't staff.

Evidence, not just alerts

Every finding includes the exact prompt and response that proved it, so fixes are obvious.

Aligned to OWASP LLM Top 10

Testing maps to the recognized standard, giving you a defensible measure of coverage.

Continuous, not one-off

Re-run on every prompt or model change so new releases don't reopen old holes.

How It Works

Point Chronoloq at your AI endpoint, chatbot, or agent.

Our engine generates and runs adversarial attacks automatically.

Successful exploits are captured with the exact prompt and response as evidence.

You receive findings mapped to the OWASP LLM Top 10 with remediation guidance.

Schedule recurring tests so every change is re-red-teamed automatically.

Frequently Asked Questions

No. The attacks are generated and run automatically, and each finding comes with plain-language remediation guidance. It's built for engineering teams that don't have a dedicated security function.

Related Guides

OWASP LLM Top 10 Checklist for SaaS

Shadow AI Detection for Finance Teams

API Discovery Tool for Compliance Teams

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