As enterprises integrate generative AI, LLM chatbots, and autonomous agents, threat actors exploit prompt injections, RAG poisoning, and unauthorized tool invocation. CyberHQ performs elite AI Red Teaming to stress-test your AI safety guardrails.
// Next-Gen Threat Surface
LLMs process natural language as code. An attacker does not need to submit complex binary exploits — they use carefully crafted conversational prompts to bypass system rules, steal private vector embeddings, or force AI agents to run malicious actions.
CyberHQ's AI security specialists design custom adversarial payloads targeting your exact LLM pipelines, autonomous tool wrappers, vector databases, and system prompts.
Direct & Indirect Prompt Injections (manipulating AI via user inputs or untrusted web data)
System Prompt Extraction & Sensitive Data Exfiltration from RAG Knowledge Bases
Unauthorized Autonomous Tool Execution (forcing agents to send emails, execute SQL, or trigger APIs)
Training Data & Vector Embedding Poisoning Attacks
[*] Injecting Base64 encoded payload into user chat session...
[!] LLM guardrail filtered initial keyword probe
[*] Crafting multi-turn cognitive context jailbreak...
[CRITICAL] System prompt successfully extracted (450 tokens leaked)
[*] Testing Indirect Prompt Injection via uploaded PDF...
[CRITICAL] RAG poisoned: AI agent executed unauthorized refund API call
[+] Testing Model Inversion on proprietary embeddings...
[HIGH] Internal customer records retrievable without auth
[✓] Red Team Assessment Complete: 3 Critical Vulnerabilities Found
// Industry Standard Coverage
We systematically audit your AI implementations against the globally recognized OWASP GenAI framework.
OWASP LLM Top 10 & Adversarial Model Security Framework
Cataloging LLM models, vector databases, autonomous tool wrappers, system prompts, and training data integrations.
Executing automated and manual adversarial payloads — testing multi-language obfuscation, roleplay framing, ASCII attacks, and context overflow.
Testing indirect prompt injections embedded in uploaded files, websites, and emails to trick the RAG pipeline into leaking unauthorized records.
Testing whether autonomous agent tools (function calling, database queries, code execution) can be hijacked to perform actions without human approval.
Delivering customized NeMo Guardrails, system prompt rewrites, and input sanitization filters, accompanied by a full verification re-test.
Every engagement includes executive briefings, technical PoCs, code-level fix guidance, and a complimentary 30-day verification re-test.
// Proven Security Impact
How an indirect prompt injection attack was neutralized before malicious actors could trigger unauthorized money transfers.
A digital banking startup deployed an autonomous customer support agent equipped with function calling to check balances and initiate fee refund requests up to $500.
By embedding a hidden prompt in a dispute invoice PDF, an attacker tricked the AI into ignoring system bounds and executing refund API calls to arbitrary accounts without human approval.
CyberHQ designed dual-layered guardrails with human-in-the-loop verification for financial calls and secondary prompt validation, eliminating 100% of exploit paths.
// Test Your AI Systems
Ensure your AI chatbots, LLM tools, and vector databases are secure against prompt injections and data leaks.