Protect your AI systems, LLM applications, and AI-powered infrastructure from adversarial attacks, prompt injection, and model exploitation with CYBERDUDEBIVASH's AI-native security platform.
Full coverage of every OWASP LLM Top 10 vulnerability class with automated detection and manual expert validation.
Direct and indirect prompt injection attacks that manipulate LLM behavior via malicious user inputs or data sources.
Downstream vulnerabilities arising from insufficient validation of LLM-generated content before it reaches sensitive systems.
Attacks targeting the data pipeline, model training, or fine-tuning to introduce backdoors or corrupt model behavior.
Adversarial inputs designed to cause resource exhaustion, degrade model performance, or trigger excessive computation.
Risks from third-party models, datasets, plugins, or pre-trained components with compromised integrity.
Unintended leakage of sensitive data, training data, system prompts, or confidential business context via LLM outputs.
LLM plugin and extension security flaws enabling unauthorized access, privilege escalation, or data exfiltration.
LLM-powered agents performing harmful actions beyond their intended scope due to inadequate permission controls.
Organizational risk from uncritical dependence on LLM outputs without adequate verification for high-stakes decisions.
Unauthorized extraction of model architecture, weights, or training data through API abuse, side-channel attacks, or membership inference.
End-to-end AI security services from assessment through continuous monitoring.
Adversarial testing of AI systems using automated attack simulation, manual expert testing, and MITRE ATLAS framework-based attack scenarios.
Comprehensive direct and indirect prompt injection testing across RAG systems, agent frameworks, and multi-modal AI applications.
AI risk assessment aligned with NIST AI RMF, ISO 42001, EU AI Act, and India DPDP Act 2023 requirements for enterprise AI deployments.
Deep security evaluation of custom LLMs, fine-tuned models, and AI pipelines including training data security and model integrity verification.
Security assessment of LLM-based autonomous agents, tool-calling chains, and multi-agent systems for excessive agency and privilege escalation risks.
Continuous monitoring of AI system security posture, automated vulnerability detection, and real-time AI threat intelligence integration.
AI security refers to the discipline of protecting artificial intelligence systems, machine learning models, and AI-powered applications from adversarial attacks, data breaches, model theft, and misuse. As organizations increasingly deploy LLMs (Large Language Models), autonomous AI agents, and AI-powered decision systems, the attack surface has expanded dramatically.
Traditional cybersecurity controls — firewalls, WAFs, endpoint detection — were not designed to address AI-specific vulnerabilities like prompt injection, training data poisoning, or model extraction attacks. AI security requires a specialized approach that understands both the technical architecture of AI systems and the adversarial techniques used to exploit them.
The OWASP LLM Top 10 is the industry standard for understanding and addressing the most critical security risks in LLM applications. Published by OWASP, it provides a prioritized framework covering vulnerabilities from prompt injection (LLM01) through model theft (LLM10). Organizations deploying ChatGPT, Claude, Gemini, or custom LLMs should conduct formal OWASP LLM assessments before and after deployment.
CYBERDUDEBIVASH AI Security Hub provides automated OWASP LLM Top 10 scanning via our AI Security Assessment platform, combined with expert human validation for complex attack scenarios that automated tools cannot reliably detect.
Prompt injection remains the most prevalent and dangerous AI security vulnerability. In a direct prompt injection attack, a malicious user crafts input that overrides the system prompt and manipulates the LLM's behavior. In an indirect prompt injection attack, malicious instructions are embedded in external data sources (web pages, documents, database records) that the LLM retrieves and processes.
Our assessment methodology covers both attack vectors with hundreds of test cases, including multi-turn conversation attacks, context manipulation, instruction hierarchy bypass, and jailbreak attempts against proprietary safety guardrails.
AI red teaming applies adversarial thinking to AI systems — systematically attempting to break, manipulate, or misuse AI applications to identify vulnerabilities before attackers do. Our AI red team uses the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework to map attack scenarios to real-world threat actor techniques.
Red team exercises cover model extraction, membership inference attacks, adversarial examples (for vision models), data poisoning simulation, and AI supply chain compromise scenarios.
Regulatory pressure on AI systems is intensifying. The EU AI Act classifies high-risk AI systems requiring mandatory security assessments. India's DPDP Act 2023 includes provisions affecting AI systems that process personal data. NIST AI RMF provides a governance framework for responsible AI deployment.
CYBERDUDEBIVASH provides structured AI governance assessments aligned with NIST AI RMF, ISO 42001, EU AI Act requirements, and India DPDP Act 2023, with automated compliance tracking and RoPA generation for AI data processing activities.
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