Illustrative Sample · No Client Data
AI / LLM Security Assessment — Sample Report
Illustrative report covering prompt injection, RAG security, tool/agent abuse, data exposure, authorization, and remediation planning.
CYRAVEX SECURITYOWASP Top 10 for LLM Aligned
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.
Comprehensive Audit Scope
What Cyravex Assesses
We evaluate your entire AI technology stack—from input guardrails and context construction to API boundaries and vector database authorization.
Prompt & Model Security
- 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)
RAG & Data Protection
- Vector database multi-tenant isolation testing
- Context poisoning & embedding manipulation risks
- System prompt & sensitive PII context disclosure
- Data retention & telemetry logging security
Agent & Tool Execution
- Insecure tool execution & parameter tampering
- Excessive agency & unauthorized API function calls
- Human-in-the-loop authorization control reviews
- Autonomous loop amplification & side-effect bounds
Application & API Layer
- Authentication, authorization & session separation
- API rate limiting & anti-automation controls
- Cross-site scripting (XSS) via markdown rendering
- Cloud API key & model endpoint credential exposure
Integrated Engineering
AI Engineering ServicesBuilding an AI Copilot or Automated Document Pipeline?
Cyravex Engineering specializes in architecting secure RAG pipelines and enterprise AI agent systems.
AI Assessment Deliverables
1. Attack Surface Map
Visual map of LLM boundaries, context feeds, tools, and vector databases.
2. Exploit Narratives
Step-by-step reproduction steps for confirmed injection & isolation flaws.
3. Code Remediation
Practical architecture & code guidance for input sanitization and tool guardrails.
4. Retest Validation
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.
DIRECT SENIOR ENGAGEMENT
Ready to Secure Your AI Product?
Talk to a senior security engineer specializing in LLM application security and vector pipeline defense.
NDA available when appropriate • Written scope & engagement controls • Clear proposal process