Specialized security assessments for AI systems, machine learning models, and LLM integrations. We test for prompt injection, data leakage, model abuse, and other AI-specific vulnerabilities.
As artificial intelligence and large language models become increasingly integrated into business applications, new attack vectors and security concerns emerge. Traditional security testing approaches are insufficient for AI systems, which require specialized knowledge of model behavior, prompt engineering, and AI-specific vulnerabilities. At Charlie Defense, we've developed a comprehensive methodology for testing AI systems that addresses the unique security challenges they present.
We begin by understanding your AI system's architecture, including model types, deployment methods, data flows, and integration points. We analyze how AI models are integrated into your applications, what data they process, and how they interact with other systems. This architectural analysis helps us identify potential attack surfaces and security concerns specific to your implementation.
We document the AI pipeline from data ingestion through model inference, identifying where security controls are implemented and where vulnerabilities might exist. We assess data handling practices, model versioning, access controls, and monitoring capabilities. This comprehensive understanding forms the foundation for our security testing approach.
Prompt injection attacks are among the most significant threats to LLM-based applications. We conduct extensive testing for various prompt injection techniques including direct prompt injection, indirect prompt injection, jailbreak attempts, and prompt leaking. We test how your system handles malicious user inputs designed to override system instructions, extract sensitive information, or manipulate model behavior.
We develop custom prompt injection payloads tailored to your specific application and use case. This includes testing for instruction following attacks, where malicious prompts attempt to override system prompts; prompt extraction attacks, where attackers try to reveal system instructions or training data; and jailbreak attacks, where prompts attempt to bypass safety controls or content filters.
We test both direct prompt injection (where malicious content is included in user inputs) and indirect prompt injection (where malicious content is embedded in data sources that the model processes, such as web pages, documents, or databases). Indirect prompt injection is particularly concerning as it can be harder to detect and prevent.
AI models can inadvertently leak sensitive information from training data, system prompts, or other data sources. We test for training data extraction, where attackers attempt to extract information that was included in the model's training data. We also test for prompt leakage, where system instructions or sensitive configuration information might be revealed through model outputs.
We assess how your system handles sensitive data in prompts and model outputs, testing for potential data exposure through model responses. We evaluate data retention policies, logging practices, and data handling throughout the AI pipeline to identify potential privacy violations or data leakage risks.
We test for various forms of model abuse including content generation attacks (generating harmful, illegal, or inappropriate content), automated abuse (using AI systems to generate spam, misinformation, or other malicious content at scale), and adversarial inputs designed to cause model failures or incorrect outputs.
We assess your content filtering and safety controls, testing how well they prevent generation of harmful content. We test for bypass techniques that might allow generation of content that should be blocked. We also test for rate limiting and abuse prevention mechanisms to ensure your system cannot be easily abused at scale.
Adversarial inputs are carefully crafted inputs designed to cause AI models to produce incorrect or unexpected outputs. We test for adversarial examples that might cause classification errors, generate inappropriate responses, or bypass safety controls. We assess your model's robustness to adversarial inputs and test mitigation strategies.
We test for various types of adversarial attacks including evasion attacks (inputs designed to be misclassified), poisoning attacks (malicious training data), and model extraction attacks (attempts to extract model architecture or parameters). While some of these attacks may be more relevant during model development, we assess your deployed systems for vulnerabilities to these attack types.
While AI-specific vulnerabilities are important, we also assess the security of AI system integrations and infrastructure. This includes testing API security, authentication and authorization mechanisms, data transmission security, and infrastructure security controls. We ensure that traditional security vulnerabilities don't compromise your AI systems.
We assess how AI systems integrate with other applications and services, testing for vulnerabilities in API endpoints, authentication mechanisms, and data flows. We test for traditional web application vulnerabilities in AI-powered applications, ensuring that AI integration doesn't introduce new attack vectors or weaken existing security controls.
AI systems often process sensitive data and may be subject to various compliance requirements. We assess your AI systems for compliance with relevant regulations including GDPR (for data processing), industry-specific regulations, and AI governance frameworks. We evaluate data handling practices, user consent mechanisms, and transparency requirements.
AI security testing requires specialized tools and techniques beyond traditional security testing tools. We utilize a combination of custom-developed tools, open-source frameworks, and manual testing techniques.
We've developed custom frameworks for testing prompt injection vulnerabilities, including libraries of injection payloads, automated testing tools, and manual testing methodologies. These tools help us systematically test for various prompt injection attack vectors.
Custom tools and methodologies for testing jailbreak attacks and safety control bypasses. We test various jailbreak techniques including role-playing prompts, hypothetical scenarios, and other techniques designed to bypass content filters and safety controls.
Tools for testing training data extraction and prompt leakage vulnerabilities. We use techniques including membership inference attacks, model inversion attacks, and prompt extraction methodologies to identify data leakage risks.
We utilize frameworks like Adversarial Robustness Toolbox (ART) and custom tools for generating and testing adversarial inputs. These tools help us assess model robustness and identify vulnerabilities to adversarial attacks.
Burp Suite and custom tools for testing AI API security. We test for traditional API vulnerabilities in AI-powered applications, including authentication flaws, authorization issues, and input validation problems.
Tools for analyzing model behavior, understanding model outputs, and identifying potential security issues. We use various techniques to understand how models process inputs and generate outputs, helping identify security vulnerabilities.
Tools for analyzing model outputs, testing content filtering effectiveness, and identifying potential abuse vectors. We assess how well safety controls prevent generation of harmful content.
We develop custom scripts and tools tailored to your specific AI implementation, testing for application-specific vulnerabilities and security issues that standard tools cannot identify.
AI security is a rapidly evolving field, and traditional security testing approaches are insufficient for AI systems. Our team has deep expertise in both security testing and AI systems, allowing us to identify vulnerabilities that other security firms might miss. We understand how AI models work, how they can be attacked, and how to secure them effectively.
We stay current with the latest AI security research and attack techniques, ensuring our testing methodologies reflect the current threat landscape. We've tested various AI implementations including LLM-powered applications, computer vision systems, and machine learning models, giving us broad experience across different AI technologies.
Our reports are designed to be actionable and immediately useful. We don't just identify vulnerabilities. We explain how attackers would exploit them, what the business impact would be, and exactly how to fix the issues. We provide specific, implementable recommendations for securing AI systems, not just generic security advice.
We understand that AI security is an ongoing concern, not a one-time assessment. We provide guidance for building security into your AI development lifecycle, helping you maintain security as your AI systems evolve and new threats emerge.
Schedule a consultation to discuss your AI security testing needs.