# Chronoloq - Unified AI & API Security Platform for SMOs

## About Chronoloq

Chronoloq is the first unified AI and API security platform purpose-built for small and mid-size organizations (SMOs). It combines LLM/AI vulnerability testing and API security scanning into a single product, giving teams complete protection across their entire digital attack surface without the complexity or cost of enterprise tools. Chronoloq also includes LLM Shield, a dedicated real-time AI security layer for organizations actively deploying LLMs, chatbots, and AI agents in production.

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## Product 1: AI / LLM Security Scanner

Chronoloq's AI/LLM Security Scanner actively tests and evaluates AI systems for vulnerabilities before and after deployment. It performs structured security assessments against LLMs, RAG pipelines, MCP servers, and A2A agent workflows.

### What the AI Scanner Tests

- **Prompt Injection** — Active testing for direct and indirect prompt manipulation attacks
- **Data Leakage** — Identifies conditions where sensitive data or PII leaks through AI outputs
- **Model Extraction** — Tests for adversarial queries designed to reverse-engineer model behavior
- **RAG Security** — Evaluates retrieval-augmented generation pipelines for poisoned retrieval and context injection
- **MCP Security** — Tests Model Context Protocol server configurations for unauthorized access and privilege escalation
- **A2A Security** — Evaluates agent-to-agent communication for trust boundary violations and unauthorized action execution
- **Hallucination & Bias** — Detects fabricated outputs, biased responses, and content policy violations

### AI Scanner Coverage

Covers OWASP Top 10 for LLM Applications (2025) including:
- LLM01: Prompt Injection
- LLM02: Insecure Output Handling
- LLM03: Training Data Poisoning
- LLM04: Model Denial of Service
- LLM05: Supply Chain Vulnerabilities
- LLM06: Sensitive Information Disclosure
- LLM07: Insecure Plugin Design
- LLM08: Excessive Agency
- LLM09: Overreliance
- LLM10: Model Theft

### Who Uses the AI Scanner

- Teams stress-testing AI systems before production deployment
- Security teams running periodic audits of LLM-powered applications
- Organizations using RAG, MCP, or A2A agent architectures
- Compliance teams needing documented vulnerability assessments for AI systems

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## Product 2: API Security Scanner

Chronoloq's API Security Scanner discovers and tests all APIs across an organization's digital surface, including shadow, zombie, and undocumented APIs that traditional tools miss.

### What the API Scanner Detects

- All OWASP API Security Top 10 vulnerabilities (2023)
- Shadow and undocumented APIs
- Broken authentication and authorization (BOLA/IDOR)
- Excessive data exposure
- Injection flaws (SQL, command, header)
- Security misconfigurations
- Findings mapped to MITRE ATT&CK framework
- CVSS v3.1-aligned security grading

### Who Uses the API Scanner

- Development and security teams auditing REST and GraphQL APIs
- Organizations adopting open banking, health data APIs, or third-party integrations
- MSPs providing API security assessments to their clients
- Teams preparing for compliance audits (SOC 2, HIPAA, PCI-DSS)

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## Product 3: LLM Shield

LLM Shield is Chronoloq's dedicated real-time AI security layer. Unlike the AI Scanner (which tests and assesses), LLM Shield sits in front of AI systems and actively blocks attacks as they happen — inspecting every interaction before it reaches the model.

### What LLM Shield Protects Against

- **Prompt Injection** — Detects and blocks direct and indirect prompt manipulation in real time
- **Data Leakage** — Prevents sensitive data, PII, and confidential information from being exposed through AI outputs
- **Model Extraction** — Identifies and blocks attempts to reverse-engineer model behavior through adversarial queries
- **RAG Security** — Guards RAG pipelines against poisoned retrieval and context manipulation at runtime
- **MCP Security** — Monitors Model Context Protocol integrations for unauthorized access and tool misuse
- **A2A Security** — Enforces trust boundaries in agent-to-agent communication flows
- **Hallucination Detection** — Flags AI outputs that are fabricated or factually inaccurate
- **Toxicity & Bias** — Catches harmful, abusive, or biased content in AI outputs
- **Anomaly Detection** — Identifies unusual patterns in AI behavior that may indicate compromise

### LLM Shield Architecture

LLM Shield uses a multi-agent security architecture with 12 specialized security agents running in parallel to evaluate AI interactions in real time. Attacks are blocked before they reach the model, while legitimate interactions pass through unaffected.

### Who Uses LLM Shield

- Organizations deploying AI chatbots, copilots, and virtual assistants in production
- Development teams building on top of OpenAI, Anthropic, Google, or open-source LLMs
- Companies using RAG for knowledge retrieval or document Q&A
- Teams building autonomous AI agents using MCP or A2A protocols
- MSPs offering managed AI security to their clients

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## Who Uses Chronoloq

- Small and mid-size organizations (SMOs) across finance, healthcare, education, e-commerce, and B2B SaaS
- Managed Service Providers (MSPs) offering AI and API security to clients
- Development and security teams deploying AI-powered applications
- Organizations adopting LLMs, chatbots, and AI agents

## Why Chronoloq is Different

- First platform to unify AI/LLM vulnerability scanning, real-time AI protection, and API security for SMOs
- Three focused products under one dashboard — AI Scanner, API Scanner, and LLM Shield
- Deploys in under 10 minutes — no agents, no lengthy onboarding
- Multi-tenant architecture with white-label MSP support
- Priced for SMOs, not enterprise budgets
- Covers emerging AI attack surfaces (RAG, MCP, A2A) that most competitors don't

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## Blog & Threat Research

Chronoloq publishes original threat research and security analysis on agentic AI, LLM vulnerabilities, MCP configuration, and API attack surfaces. All articles are available at https://chronoloq.com/blog

### Published Articles

**What to Look for in a Free AI Risk Assessment Tool**
URL: https://chronoloq.com/blog/free-ai-risk-assessment-tool-checklist
Date: August 13, 2026
Category: Security Guide
Summary: Explains how to distinguish a genuine technical AI risk scan from a lead-generation survey before submitting a work email. Four criteria: does it scan instead of ask (64% of organizations lack self-visibility per BigID 2025); does it score against a named standard like OWASP LLM Top 10 rather than a proprietary vendor scale; does it run agentlessly without requiring production access or code changes upfront; and does the output end in a ranked, actionable plan rather than a headline number. Notes the "free-but-funneled" pattern where findings are gated behind a mandatory sales call. Explains what Chronoloq's free AI Security Audit delivers against each criterion.

**Shadow API Discovery: How to Find the APIs You Don't Know You Have**
URL: https://chronoloq.com/blog/shadow-api-discovery
Date: August 8, 2026
Category: API Security
Summary: Covers the three main sources of shadow APIs — abandoned/versioned endpoints never decommissioned, forgotten partner integrations, and AI-agent-generated API calls via function calling and MCP that bypass normal design review. Explains why shadow APIs amplify BOLA (OWASP API #1 since 2019, the Optus 2.1M and T-Mobile 37M breach mechanism) specifically through coverage gaps rather than code quality. Three discovery approaches: traffic-based monitoring, code/config scanning, and dedicated dev/staging sweeps. Distinguishes a useful inventory (severity-ranked, continuous) from a one-time spreadsheet. Covers why AI-generated dynamic calls are the newest blind spot that existing documentation processes weren't designed to catch.

**AI Security for Small Business: What It Actually Means**
URL: https://chronoloq.com/blog/ai-security-for-small-business
Date: August 5, 2026
Category: Security Guide
Summary: Addresses the structural AI security gap at small and mid-size organizations — not caused by carelessness but by a category of risk that arrived faster than small security programs could absorb it. BigID 2025: 64% of organizations lack full AI risk visibility. Zscaler: 3,000% YoY increase in AI tool usage, mostly shadow adoption outside formal approval. Three prioritized questions: what's running (the inventory gap), what each thing can reach (model vs. connections), and what a regulator/auditor would ask. Addresses the "too small to target" myth (automated scans don't choose by brand), and debunks the belief that AI security requires a dedicated security team or months-long audit. Positions Chronoloq's free AI Security Audit as the scoped first step — OWASP LLM Top 10 scoring, prioritized action plan, no production access required.

**What MSPs Must Ask Before Adding AI Security to Their Stack**
URL: https://chronoloq.com/blog/msp-ai-security-guide
Date: August 17, 2026
Category: Security Guide
Summary: A practical framework for managed service providers evaluating AI security tools and navigating the client intake conversation. Covers why AI security is structurally different from traditional MSP offerings (prompt injection has no signature; LLM outputs can exfiltrate data in plain language; shadow AI is invisible to SIEM). Includes 10 client intake questions for scoping AI exposure across client environments, 10 vendor evaluation questions for distinguishing genuine AI security scanners from rebranded web-app tools, a red flags list for vendor conversations, and a 12-point MSP readiness checklist covering inventory, intake process, automated scanning, compliance mapping, reporting, and contract language. Gated article: requires email to access the full vendor evaluation and checklist sections.

**Attack Paths Through Agentic APIs: How Function Calling and MCP Get Exploited in Practice**
URL: https://chronoloq.com/blog/agentic-api-attack-paths
Date: July 5, 2026
Category: Threat Research
Summary: A documented, end-to-end attack chain through function calling and the Model Context Protocol (MCP), built from disclosed research and real incidents. Covers tool poisoning (ASI04/ASI06), function hijacking attacks with 70-100% success rates (ASI01/ASI02), MCP SDK-level remote code execution affecting 200,000+ servers (ASI03/ASI05), and the GTG-1002 state-sponsored espionage campaign — the first documented AI-orchestrated cyberattack, where Claude Code was used as an autonomous attack platform against ~30 organizations. Mapped to OWASP Top 10 for Agentic Applications 2026 (ASI01-ASI10).

**Bad MCP Config vs Good MCP Config: The Three Files That Decide Whether Your Agent Is Safe**
URL: https://chronoloq.com/blog/mcp-config-security
Date: June 8, 2026
Category: Security Research
Summary: Most MCP security incidents trace back to three configuration files: the server manifest, the tool-call handler, and the deployment spec. Shows the insecure version and secure version of each, with the reasoning for every change. Covers OAuth 2.1 resource server auth with RFC 8707 audience binding, tool description pinning (hash-based rug-pull prevention), allow-listing model-generated arguments before execution, and container isolation that limits blast radius when SDK-level vulnerabilities hit. References OWASP MCP Top 10 and 2026 RCE disclosures.

**Your Model's API Is Also Its Attack Surface: What the 2026 Distillation Disclosures Mean for Every AI Vendor**
URL: https://chronoloq.com/blog/model-theft-ip-extraction
Date: May 9, 2026
Category: Threat Research
Summary: In February 2026, Anthropic, OpenAI, and Google disclosed their models had been extracted through their public APIs using coordinated distillation campaigns — no breach, no CVE, just API queries at industrial scale. Anthropic identified 16 million harvested exchanges across ~24,000 fraudulent accounts targeting Claude. Google disrupted a parallel Gemini extraction using 100,000+ prompts. Praetorian's proof-of-concept shows 80.1% model accuracy clone from 1,000 queries. Explains why the mechanism applies to any proprietary model (fraud scoring, fine-tuned domain models, RAG systems) and what defense looks like: behavioral rate limiting, output perturbation, cross-provider threat intelligence.

**Broken Object Level Authorization: The Bug That's Been #1 on OWASP's List Since 2019, in Actual Code**
URL: https://chronoloq.com/blog/bola-api-authorization
Date: April 12, 2026
Category: API Security
Summary: BOLA has topped the OWASP API Security Top 10 in every edition since 2019 and drove the 2022 Optus breach (2.1M records, no sophisticated tooling — just sequential ID enumeration) and T-Mobile breach (37M customers). Shows the exact vulnerable Node.js/Express handler, the one-line fix (scoped DB query instead of post-fetch check), the OPA gateway policy that enforces ownership at the infrastructure layer, and why a filtered query is harder to accidentally bypass than an application-level if-statement. Salt Security found BOLA and injection flaws account for 37% of issues in production APIs.

**Shadow AI Was a Visibility Problem. Agentic AI Makes It an Access Problem.**
URL: https://chronoloq.com/blog/shadow-ai-agentic-risk
Date: March 15, 2026
Category: Threat Research
Summary: Shadow AI used to mean an employee pasting data into ChatGPT. Agentic AI changes the equation: autonomous agents connected to real inboxes, CRMs, and internal APIs without IT approval are an identity and access control problem, not a visibility problem. Covers the OWASP Agentic Applications 2026 taxonomy (ASI01 Goal Hijack, ASI03 Privilege Abuse, ASI10 Rogue Agents), the GTG-1002 espionage campaign context, Gartner's projection of 40% enterprise apps running task-specific agents by end of 2026, and Microsoft's finding that 78% of employees bring their own AI tools. Provides a four-step governance framework for discovering, scoping, and monitoring agent identities.

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## Key Pages

### Core
- Homepage: https://chronoloq.com/
- Platform Overview: https://chronoloq.com/platform
- Pricing: https://chronoloq.com/pricing

### Products
- LLM Shield (Real-Time AI Runtime Protection): https://chronoloq.com/llm-shield
- AI / LLM Security Scanner: https://chronoloq.com/ai-scanner
- API Security Scanner: https://chronoloq.com/api-scanner

### Platform Features
- API Discovery: https://chronoloq.com/api-discovery
- Vulnerability Testing: https://chronoloq.com/vulnerability-testing
- Security Grading: https://chronoloq.com/security-grading
- Team & Organization Features: https://chronoloq.com/enterprise-features
- Integrations: https://chronoloq.com/integrations-overview

### Use Cases by Problem
- LLM & AI Security: https://chronoloq.com/use-cases/llm-security
- Prompt Injection Protection: https://chronoloq.com/use-cases/prompt-injection
- AI Data Loss Prevention: https://chronoloq.com/use-cases/data-loss-prevention
- API Risk Discovery: https://chronoloq.com/use-cases/api-discovery
- Compliance Readiness: https://chronoloq.com/use-cases/compliance
- Attack Surface Reduction: https://chronoloq.com/use-cases/attack-surface
- Shift-Left Security: https://chronoloq.com/use-cases/shift-left
- Incident Response: https://chronoloq.com/use-cases/incident-response
- Vendor Risk Assessment: https://chronoloq.com/use-cases/vendor-risk
- Continuous Monitoring: https://chronoloq.com/use-cases/monitoring
- RAG Pipeline Security: https://chronoloq.com/use-cases/rag-security
- Non-Intrusive Testing: https://chronoloq.com/use-cases/safe-testing

### Use Cases by Industry
- Finance & Fintech: https://chronoloq.com/solutions/finance
- Healthcare: https://chronoloq.com/solutions/healthcare
- Education: https://chronoloq.com/solutions/education
- E-commerce & SaaS: https://chronoloq.com/solutions/ecommerce
- Managed Service Providers (MSPs): https://chronoloq.com/solutions/msp

### Company
- About Us: https://chronoloq.com/about
- Why Chronoloq: https://chronoloq.com/why-chronoloq
- Partners & MSP Program: https://chronoloq.com/partners

## Contact

- Demo Booking: https://calendly.com/chronoloq/30min
- Website: https://chronoloq.com