AI & LLM Security Assessment
Rigorous offensive testing and architectural review for LLM-powered applications, RAG memory stores, vector databases, and autonomous AI agents. Discover injection vectors before attackers exploit them.
Why AI Security Demands Specialized Engineering
AI applications bridge non-deterministic natural language logic with deterministic system APIs. Security failures occur when untrusted text controls code execution.
Direct & Indirect Prompt Injection
Adversaries manipulate system instructions directly via user prompts or indirectly via malicious data ingested from emails, PDFs, or scraped websites into vector stores.
Excessive Agency & Insecure Tool Execution
When LLM agents are given autonomous tools (database writes, API webhooks, shell execution), prompt injection can trigger unauthorized system state modifications.
RAG Context Poisoning & Isolation Leakage
Vector databases often lack row-level multi-tenant isolation, allowing cross-user data leakage when context embeddings are retrieved into shared prompt windows.
What Cyravex Assesses
We evaluate your entire AI technology stack—from input guardrails and context construction to API boundaries and vector database authorization.
- Direct prompt injection & system prompt exfiltration
- Indirect prompt injection in documents & external feeds
- Jailbreak resistance & safety guardrail bypass analysis
- Model denial of service (unbounded context amplification)
- Vector database multi-tenant isolation testing
- Context poisoning & embedding manipulation risks
- System prompt & sensitive PII context disclosure
- Data retention & telemetry logging security
- Insecure tool execution & parameter tampering
- Excessive agency & unauthorized API function calls
- Human-in-the-loop authorization control reviews
- Autonomous loop amplification & side-effect bounds
- Authentication, authorization & session separation
- API rate limiting & anti-automation controls
- Cross-site scripting (XSS) via markdown rendering
- Cloud API key & model endpoint credential exposure
AI Assessment Deliverables
Visual map of LLM boundaries, context feeds, tools, and vector databases.
Step-by-step reproduction steps for confirmed injection & isolation flaws.
Practical architecture & code guidance for input sanitization and tool guardrails.
Re-evaluation of remediated vulnerabilities to issue final verified report.
AI Security FAQs
Common questions regarding AI application security testing.
Traditional web pentests evaluate HTTP headers, standard SQLi, and basic auth. AI applications introduce non-deterministic execution layers, vector database store poisoning, indirect prompt injections embedded in ingested PDFs/web scrapes, and tool-execution hijacking where an LLM is granted excessive agency to call system APIs.
Ready to Secure Your AI Product?
Talk to a senior security engineer specializing in LLM application security and vector pipeline defense.