AI Security & Engineering
Test language models and AI agents for prompt injection, sensitive data exposure, unsafe tool use, and excessive permissions. Design and prototype local or private AI workflows with strict access controls, evaluation, and human oversight.
Where New Capabilities Create New Vulnerabilities
Connecting generative AI models to tools, databases, and APIs turns natural language into an executable control plane.
Securing Autonomous Agents & Private Deployments
Autonomous AI agents and large language models introduce failure modes that cannot be caught by conventional vulnerability scanners. Direct prompt injection, indirect context contamination from untrusted web pages, vector database retrieval poisoning, and unconstrained API tool invocation create pathways for complete system compromise.
At CROW Innovations, we combine offensive testing of generative models with hands-on AI systems engineering. We evaluate where agent boundaries fail under adversarial pressure, and we architect sovereign, private, or air-gapped AI environments that enable teams to leverage frontier intelligence while guaranteeing that client data never leaves approved infrastructure.
AI Security Vectors & Engineering Disciplines
Comprehensive evaluation of models, autonomous pipelines, and private inference infrastructure.
Prompt Injection & Jailbreak Testing
Evaluating model vulnerability to direct jailbreaks, role-play evasion, multi-language bypasses, token smuggling, and indirect prompt injection through external documents or web crawls.
Autonomous Agent Tool Sandboxing
Testing agent execution boundaries, function calling protocols, Model Context Protocol (MCP) integrations, and verifying that AI agents cannot execute unauthorized commands or SSRF requests.
RAG Poisoning & Vector Store Integrity
Assessing retrieval-augmented generation pipelines for embedding manipulation, semantic similarity hijack, document store poisoning, and unauthorized cross-tenant data retrieval.
Sovereign & Air-Gapped AI Architecture
Prototyping and deploying local model workflows using quantized open-weights models (Ollama, vLLM, TensorRT-LLM) deployed entirely on isolated, air-gapped on-premises hardware.
Sensitive Data Exposure & Privacy
Testing models for memorized training data extraction, PII leaks, credential regurgitation, and evaluating differential privacy protections across fine-tuned weights.
Defensive Guardrails & Human-in-the-Loop
Designing layered, deterministic validation wrappers around model inputs and outputs, ensuring that critical operations require explicit human confirmation.
AI Security Deliverables & Artifacts
Specific technical outputs for ML engineers, system architects, and governance officers.
Adversarial Prompt Attack Matrix
Comprehensive test suite of prompt injection payloads, evasion variations, and recorded model responses.
Agent Tool Boundary Audit
Technical review of function calling schemas, tool invocation permissions, and sandbox escape potential.
Private AI Architecture Plan
Detailed deployment specification for air-gapped or VPC-contained local inference with zero third-party telemetry.
Defensive Guardrail Synthesis
Deterministic input/output filtering rules and human-in-the-loop policies to mitigate identified failure modes.
Tools, Projects & Developments
Autonomous agent harnesses, private inference testbeds, and adversarial prompt generation utilities developed by CROW Innovations.
Automated Prompt Injection Fuzzer
Autonomous fuzzing harness that generates iterative semantic variations of adversarial prompt injections to test LLM system prompt resilience.
Air-Gapped Sovereign AI Architecture
Reference architecture for deploying local open-source models inside high-security government and defense enclaves without network egress.
Agent Tool Sandboxing Research
Technical findings and benchmarks evaluating how LLMs react to conflicting tool instructions and malicious schema injections.