Security research, written for practitioners
Technical guides, threat breakdowns, and compliance references from the Vynox Security research team — covering AI security testing and infrastructure penetration testing.
AI Security Testing
What Is Prompt Injection? A Technical Deep-Dive for AI Engineers
Prompt injection is the #1 vulnerability in the OWASP LLM Top 10 — and for good reason. It is trivially exploitable, broadly applicable, and capable of enabling data exfiltration, privilege escalation, and full agent…
How Attackers Exfiltrate RAG Knowledge Bases: 5 Techniques and Defenses
Retrieval-Augmented Generation (RAG) lets LLM applications answer questions using private, up-to-date knowledge — internal documentation, customer records, product data — without embedding that data in the model…
AI Agent Hijacking: How Autonomous Workflows Get Compromised
Agentic AI systems — autonomous workflows where an LLM plans, decides, and acts across multiple steps using tools like web search, email, code execution, and database access — represent a fundamentally different…
What Is Indirect Prompt Injection? The Most Underrated AI Vulnerability
If direct prompt injection is the AI equivalent of SQL injection, indirect prompt injection is the AI equivalent of stored XSS — more dangerous, harder to detect, and more widely exploitable at scale. Despite this,…
LLM Security Testing Checklist: 20 Tests Before Launch
Before shipping an LLM-powered product, your team should run a minimum security test suite covering the most commonly exploited vulnerability categories. This checklist is mapped to the OWASP LLM Top 10 and is…
What Is Model Inversion? How Attackers Extract Training Data from Fine-Tuned LLMs
When you fine-tune an LLM on proprietary data — customer support transcripts, internal documentation, medical records, financial data — that data doesn't disappear into the model's weights invisibly. Under certain…
Infrastructure Testing
Web Application Penetration Testing: A Complete Guide for Engineering Teams
Web application penetration testing is one of the most mature disciplines in offensive security — and one of the most frequently misunderstood. Teams often confuse it with automated vulnerability scanning,…
The 10 Most Common AWS Misconfigurations That Lead to Data Breaches
Cloud misconfigurations are the leading cause of data breaches in organisations running workloads on AWS. Unlike traditional vulnerabilities that require exploitation of a software flaw, misconfigurations are often a…
Mobile App Security Testing: What iOS and Android VAPT Actually Covers
Mobile applications present a security testing challenge that web applications don't: the client is in the hands of the attacker. On a mobile device, an attacker can run the app in a controlled environment,…
API Security Testing: OWASP API Top 10 and How to Test Each Vulnerability
APIs are the attack surface most likely to be undertested in your application. While web application firewalls and front-end security get significant attention, the API layer — where your actual business logic and…
Penetration Test vs. Vulnerability Scan: What Is the Difference?
These two terms are used interchangeably in sales calls, compliance checklists, and casual conversation — but they describe fundamentally different activities with different outputs, different costs, and different…
Compliance
SOC 2 Type II for AI Startups: Mapping LLM Security Controls to Your Trust Framework
SOC 2 was designed for cloud software companies. Its five Trust Services Criteria — Security, Availability, Processing Integrity, Confidentiality, and Privacy — provide a solid baseline for general software security,…
EU AI Act Security Requirements: What High-Risk AI Systems Must Test
The EU AI Act is the world's first comprehensive legal framework for artificial intelligence. It introduces binding requirements for AI systems across risk tiers — and for companies building or deploying AI in the…
ISO 27001 for AI Systems: Extending Your ISMS to Cover LLMs and ML Pipelines
ISO 27001:2022 is the international standard for information security management systems. The 2022 revision added 11 new controls to Annex A that have significant relevance for organisations operating AI systems. But…
Building an AI Security Policy: A Practical Template for AI-First Startups
Most AI startups inherit a generic security policy from their SOC 2 readiness vendor and call it done. The problem is that a generic security policy doesn't mention LLMs, prompts, RAG pipelines, AI agents, or model…
Red Teaming
What Is AI Red Teaming? How Security Teams Test LLMs Before Attackers Do
Red teaming has been a staple of military and intelligence security practice for decades: assemble a team whose job is to think like the adversary and find the weaknesses your defenders have normalised. The same…
Jailbreaking vs. Prompt Injection: Key Differences Every Security Team Should Know
In conversations about LLM security, jailbreaking and prompt injection are frequently used as synonyms. They are not. Conflating them leads to misallocated security investment, incorrect threat modelling, and…
How to Build an AI Security Testing Program from Scratch
Most organisations building AI products have a security program — but most security programs weren't built to handle AI systems. Web application pentesting, vulnerability management, and cloud security posture…
Common questions about security testing
What is AI security testing?
AI security testing is the process of probing LLMs, RAG pipelines, AI agents, and model APIs for vulnerabilities — including prompt injection, training data extraction, agent hijacking, and insecure tool use — using adversarial techniques tailored to AI systems rather than traditional software.
What is the difference between a penetration test and a vulnerability scan?
A vulnerability scan is an automated tool that identifies known weaknesses from a signature database. A penetration test involves a human expert actively exploiting those weaknesses, chaining vulnerabilities, and finding logic flaws and business-context issues that no automated scanner can detect.
What is prompt injection and why is it the #1 LLM vulnerability?
Prompt injection is an attack where malicious input manipulates an LLM's instructions, causing it to ignore its system prompt, leak confidential data, or take unintended actions. It ranks #1 in the OWASP LLM Top 10 because it is both extremely common and directly exploitable in production AI applications.
How often should a company run a penetration test?
Most compliance frameworks (SOC 2, ISO 27001, PCI DSS) require at least one penetration test per year. For AI products or companies deploying significant new features, quarterly or release-gated continuous testing is more appropriate to keep pace with an evolving attack surface.
What does an AI security pentest report include?
A Vynox AI security report includes an executive summary, a complete finding list with CVSS-style severity ratings, reproduction steps, attack impact analysis, and developer-ready remediation guidance mapped to OWASP LLM Top 10 categories and your specific tech stack.
What is the difference between jailbreaking and prompt injection?
Jailbreaking bypasses a model's safety alignment guardrails to elicit harmful or restricted content. Prompt injection overrides the application's system prompt to redirect the model's behaviour toward attacker-controlled goals. They are distinct attack classes requiring different mitigations.
Do I need a pentest if I already run automated vulnerability scans?
Yes. Automated scans catch known CVEs and configuration issues, but they cannot exploit vulnerabilities in context, chain findings together, or identify business logic flaws. Most breaches exploit exactly the issues scanners miss — which is what a manual penetration test is designed to find.
What is RAG data exfiltration and how do attackers do it?
RAG exfiltration is when an attacker crafts queries that cause an LLM to retrieve and output documents from a knowledge base that were not meant to be disclosed. Common techniques include indirect prompt injection via poisoned documents, context extraction through iterative probing, and overly permissive retrieval scope configuration.
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