Mastering Generative AI Security
Mitigate the unique, critical risks posed by Large Language Models (LLMs). Deploy robust defense mechanisms against prompt injection, data poisoning, and unauthorized access.
The LLM Attack Surface: Critical Threats
Understand the novel adversarial techniques targeting large language models and how they exploit vulnerabilities.
Prompt Injection
Malicious manipulation of input prompts to bypass safety rules or security filters, leading to unintended execution or data leakage.
- Cross-site Scripting equivalents
Training Data Poisoning
Introduction of adversarial data during training to compromise model integrity, introduce bias, or create exploitable backdoors.
- Integrity and Reliability Loss
Model & IP Theft
Adversarial attempts to extract proprietary information about the model architecture or reverse-engineer sensitive training data.
- Membership Inference Attacks
Establishing Robust GenAI Governance
Effective AI deployment requires a holistic governance structure that integrates cybersecurity practices throughout the entire LLM lifecycle.
Comprehensive Threat Modeling
Identify potential vulnerabilities specific to your LLM architecture, mapping out data flow, input validation points, and trust boundaries using tools like STRIDE for AI.
Zero Trust Input Validation
Implement strict runtime monitoring and canonicalization of user inputs and model outputs to prevent unexpected function calls or data exfiltration.
Practical Guide: Defense Layers
Visualization of the Zero Trust architecture applied to the LLM interaction chain.
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