Table of Contents
Introduction
Welcome to the AI Security Research series! Whether you're a developer working with LLMs, a security professional adapting to AI threats, or simply curious about the security landscape around artificial intelligence, this series will guide you from foundational concepts to advanced research techniques.
Large Language Models (LLMs) like GPT, Claude, and others are reshaping how we interact with technology, but they also introduce entirely new attack surfaces and security challenges. Unlike traditional software security, AI security requires understanding not just code vulnerabilities, but also data poisoning, prompt manipulation, and emergent behaviors that can be exploited.
This first part establishes the foundational knowledge you'll need for the rest of the series, covering why AI security is critical and introducing the core security domains that every AI security researcher should understand.
Why AI Security Matters
The rapid adoption of LLMs in production systems has created a perfect storm of security challenges. These models are increasingly connected to sensitive enterprise data, making autonomous decisions, and directly interacting with users at scale.
Real-World Impact Examples
- Operational Damage: AI systems making incorrect decisions due to manipulated inputs
- Data Breaches: LLMs inadvertently exposing training data or user information
- Regulatory Penalties: Violations of GDPR, CCPA, and emerging AI legislation
- Reputational Loss: Public incidents involving biased or harmful AI outputs
- Trust Breakdown: Users losing confidence in AI-powered services
Studies show that incidents involving AI systems can have cascading effects, with bias, model exploitation, or data leaks leading to significant financial and reputational damage. With LLMs increasingly connected to sensitive enterprise and personal data, robust security is no longer optional—it's foundational for responsible AI adoption.
The Four Pillars of LLM Security
Understanding LLM security requires thinking across four interconnected domains. Each pillar represents a different aspect of the AI system that can be targeted or compromised.
Data Security
LLMs rely on enormous datasets for training, creating multiple points of vulnerability:
- Data Poisoning: Attackers inject malicious data into training sets, altering future LLM behavior or introducing hidden backdoors
- Sensitive Data Leakage: Personal information or corporate secrets exposed during training can later be revealed in outputs
- Bias & Disinformation: Noisy or agenda-driven data can propagate bias or falsehoods at scale
Best practices include rigorous data auditing, exclusion of PII, post-training output review, and implementing security platforms that monitor for data leaks in LLM outputs.
Model Security
This focuses on protecting the model file and its configuration:
- Model Extraction/Theft: Attackers may steal or copy LLM weights, leading to IP loss and reproducibility of vulnerabilities
- Unauthorized Modifications: Changes to model weights or architectures can introduce Trojan behavior or bias
- Model Inversion Attacks: Adversaries query deployed LLMs to reconstruct input data, compromising privacy
Mitigation involves strong access control, model integrity verification (checksums, signed binaries), and monitoring for altered execution.
Infrastructure Security
LLMs operate within complex environments—servers, APIs, plugins, and cloud resources:
- API Abuse: Exposed APIs can be targets for prompt injection, data scraping, or DoS attacks
- Infrastructure Hacking: Attackers exploit vulnerabilities in the model's runtime environment
- Third-party Plugins: LLMs integrating external tools can inherit vulnerabilities from those systems
Common defenses include input validation, API key management, firewalls, regular patching, and LLM-specific protections like content moderation and anomaly detection.
Ethical Considerations
LLMs can perpetuate or amplify harms that extend beyond technical security:
- Bias/Misinformation: Algorithmic biases and information contamination spread quickly and at scale
- Hate Speech/Unsafe Content: Without intervention, LLMs can produce discriminatory, toxic, or illegal content
- Legal Liability: Failing to address these issues exposes organizations to lawsuits and regulatory sanctions
Key controls involve dataset curation, continuous red teaming, maintaining human-in-the-loop review for high-stakes applications, and proactive ethical governance.
Common Threat Landscape
The LLM threat landscape is rapidly evolving, but several key attack categories have emerged as primary concerns:
Prompt Injection
Attacker-crafted prompts that override system intent, induce undesired completions, or leak sensitive instructions.
Training Data Poisoning
Injection of malicious samples during model training to create persistent jailbreaks or bias.
Context Poisoning
Multi-turn attacks that incrementally seed concepts to prime the LLM's context for unsafe outputs.
Information Disclosure
LLMs exposing training data, test inputs, or API secrets when prompted creatively.
Each of these attack categories will be explored in detail in subsequent parts of this series, with practical examples and defensive techniques.
Getting Started with Security Testing
Security testing for LLMs requires a different mindset than traditional penetration testing. Here's how to begin building your skills:
Essential Skills to Develop
- Prompt Engineering: Understanding how to craft effective prompts and how models interpret instructions
- Data Analysis: Ability to analyze training datasets for potential vulnerabilities or biases
- API Testing: Experience with testing RESTful APIs and understanding rate limiting, authentication
- Machine Learning Basics: Fundamental understanding of how neural networks and transformers work
Recommended Testing Environment
Start with these tools and platforms for safe, ethical security research:
# Install essential tools
pip install openai anthropic
pip install langchain
pip install red-team-toolkit
# Set up API keys (use dedicated testing accounts)
export OPENAI_API_KEY="your-testing-key"
export ANTHROPIC_API_KEY="your-testing-key"
# Clone security testing repositories
git clone https://github.com/OWASP/LLMTopTen
git clone https://github.com/leondz/garak⚠️ Ethical Testing Guidelines
- Only test systems you own or have explicit permission to test
- Use dedicated testing accounts and API keys
- Never attempt to access or expose real user data
- Follow responsible disclosure for any vulnerabilities found
- Respect rate limits and terms of service
Series Roadmap
This series is designed to take you from AI security fundamentals to conducting your own research. Here's what we'll cover in upcoming parts:
Prompt Injection Attacks
Deep dive into direct and indirect prompt injection techniques, with practical examples
Training Data Poisoning
Understanding backdoors, label flipping, and data integrity attacks
Echo Chamber & Context Poisoning
Advanced multi-turn attacks that exploit conversational memory
Sensitive Information Disclosure & Mitigations
Data leakage prevention and privacy protection techniques
Data Loss Prevention for Conversational AI
Practical DLP strategies and real-time monitoring techniques
Building Your Red Team Testing Methodology
Developing systematic approaches to AI security research and testing
Conclusion
AI security is a rapidly evolving field that requires both traditional cybersecurity knowledge and an understanding of unique AI-specific vulnerabilities. As LLMs become more powerful and more integrated into critical systems, the importance of robust security measures cannot be overstated.
The foundational concepts we've covered here—the four pillars of LLM security, common threat categories, and ethical testing practices—will serve as the building blocks for the more advanced techniques we'll explore in subsequent parts of this series.
In Part 2, we'll dive deep into prompt injection attacks, examining both basic and sophisticated techniques that attackers use to manipulate LLM behavior. We'll cover direct injection, indirect injection through data sources, and the latest developments in multimodal attacks.
Further Reading
Essential resources to deepen your understanding of AI security fundamentals:
Key Resources
Comprehensive list of the most critical security risks for LLM applications
Practical guide to LLM security implementation and best practices
Official framework for managing AI risks in enterprise environments