RajSecure AI
Autonomous Offensive Security & LLM Resilience Engine
A dedicated offensive security architecture engineered to evaluate Large Language Models, RAG vector pipelines, and autonomous agent workflows against sophisticated adversarial manipulation.
THE SHIFTING THREAT LANDSCAPE
Why Standard AppSec Cannot Protect Generative AI
Traditional firewalls and web application scanners inspect deterministic inputs: SQL syntax, script tags, and binary protocols. LLMs, however, operate on probabilistic semantic representations.
When an attacker introduces malicious instructions via an indirect data source—such as an uploaded PDF, a web scraping tool, or a third-party CRM entry—the model may interpret the untrusted data as control instructions, bypassing hard-coded business logic entirely.
RajSecure AI Objective
Simulate real-world adversary behavior across multi-turn reasoning chains, verify vector database isolation boundaries, and produce developer-ready mitigations before deploying models to production.
Assessment Engine Architecture
RajSecure AI Core StackGenerates hundreds of mutated prompt variants utilizing token-smuggling, role-assumption, and linguistic encoding tricks.
Evaluates multi-tenant metadata partitioning in vector databases to ensure Tenant A queries cannot retrieve Tenant B embeddings.
Intercepts agentic function calls to detect unintended shell access, unauthorized database mutations, or unauthenticated API webhooks.
OWASP TOP 10 ALIGNMENT
Core Attack Vectors We Evaluate
Systematic evaluation aligned with the OWASP Top 10 for Large Language Model Applications.
Prompt Injection & Jailbreak Resilience
Direct and indirect prompt injections crafted to circumvent safety guardrails, alter system instructions, and hijack generative outputs.
Sensitive Information Disclosure
System prompt leakage, proprietary context exfiltration, and unauthorized access to vectorized internal knowledge bases.
Excessive Agency & Tool Misuse
Autonomous agent workflows granting unchecked execution permissions (e.g. database updates, API webhooks, shell execution) without proper authorization boundaries.
Vector Store & RAG Isolation Flaws
Cross-tenant data contamination inside shared vector databases (Pinecone, pgvector, Qdrant) via manipulated similarity queries.
Test Your AI Systems Before Deployment
Book a dedicated AI Security Assessment or technical discovery session to stress-test your prompts, models, and RAG architectures under signed mutual NDA.