AI Security & Shadow AI
Your teams are already using AI. The question is whether you know which tools, what data they touch, and how attackers can exploit them. We find out.
AI Is an Attack Surface, Not Just a Productivity Tool
Every AI tool your organization adopts — from coding assistants and internal copilots to RAG-powered chatbots and automated workflows — introduces attack surfaces that traditional security testing misses entirely. Prompt injection, training data poisoning, model extraction, and insecure API chains between LLM components are real threats that we find in production environments every week.
Our threat intel team has tracked critical vulnerabilities in LMDeploy (CVE-2026-33626 SSRF), Flowise AI (CVE-2025-59528 RCE), and LangChain/LangGraph supply-chain flaws — all exploited in the wild within days of disclosure. If your business runs any AI-powered tooling, you need security testing that understands these frameworks at the code level.
Schedule an AI Security AssessmentWhat We Assess
- Shadow AI discovery and data exposure audit
- LLM and RAG pipeline penetration testing
- AI framework supply-chain risk analysis
- Prompt injection and jailbreak testing
- AI-powered application security review
- AI acceptable use policy development
Shadow AI: The Risk You Cannot See
Shadow AI is the AI equivalent of shadow IT — tools and services employees adopt without security review or IT approval. ChatGPT plugins in browsers, AI-powered code completion in developer IDEs, image generators fed with internal documents, and SaaS platforms quietly enabling AI features on existing data. Our research on North Texas businesses shows most organizations have 3-5x more AI tool usage than leadership realizes.
A shadow AI audit maps every AI tool in your environment, identifies where sensitive data flows to third-party models, evaluates compliance implications (especially for HIPAA and SOC 2 organizations), and delivers an actionable policy framework your team can enforce.
more AI tools in use than most businesses realize when we audit their environment
average time from AI CVE disclosure to active exploitation in recent LangChain and LMDeploy vulnerabilities
AI-specific vulnerabilities caught by traditional vulnerability scanners and pen tests
AI Attack Vectors We Test For
Real threats our team has identified and exploited in production AI systems
Prompt Injection
Attackers craft inputs that override system prompts, bypass safety controls, or exfiltrate data through LLM responses. We test direct injection, indirect injection via retrieved documents, and multi-step jailbreak chains.
AI Supply-Chain Attacks
Compromised model weights, poisoned training datasets, and malicious dependencies in AI frameworks like LangChain and Hugging Face. We audit your AI dependency tree and monitor for known CVEs in every component.
RAG Data Poisoning
Retrieval-Augmented Generation systems pull context from your knowledge base. If an attacker can inject content into those sources, they control what your AI tells users, customers, and internal teams.
Agent Tool Abuse
AI agents with access to tools — file systems, APIs, databases, code execution — can be tricked into performing unauthorized actions. We test whether your agent guardrails hold under adversarial conditions.
Model Extraction & Theft
Competitors or attackers can reconstruct proprietary models by systematically querying your API. We test your rate limiting, output filtering, and access controls to prevent intellectual property theft.
Data Leakage via AI
LLMs can memorize and regurgitate training data including PII, credentials, and proprietary information. We test for data leakage paths through model responses, logging, and API integrations.
Our AI Security Assessment Process
From shadow AI discovery to hardened AI operations in four phases
Discovery
Map all AI tools, APIs, and data flows across your organization. Identify shadow AI usage, third-party model integrations, and data exposure points.
Testing
Penetration test AI components for prompt injection, data poisoning, supply-chain vulnerabilities, and agent abuse using real-world attack techniques.
Analysis
Deliver findings with business impact, CVSS scores, and compliance implications. Prioritize remediation by risk and effort.
Governance
Build AI acceptable use policies, monitoring controls, and an ongoing vulnerability management program for your AI stack.
AI Security for Dallas-Fort Worth Businesses
DFW is one of the fastest-growing tech corridors in the country, and AI adoption is accelerating across every industry. From healthcare organizations using AI for patient triage to financial services firms deploying algorithmic trading and fraud detection, the attack surface is expanding faster than most security teams can map. Headquartered in McKinney, we serve businesses across Dallas, Plano, Frisco, and the entire metroplex.
Our team combines traditional penetration testing expertise with deep knowledge of AI/ML attack techniques. We track AI-specific CVEs through our threat intelligence program and publish real-time analysis when new AI framework vulnerabilities emerge — giving our clients early warning and tested remediation guidance before exploits go mainstream.
AI Security FAQ
Common questions about AI security assessments and shadow AI risk
Find Out What AI Your Business Is Really Running
Get a shadow AI discovery and risk assessment — no obligation, no sales pitch.
AI Moves Fast. Your Security Should Move Faster.
Schedule an AI security assessment and close the gap before attackers exploit it.
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