AI Security Risks: A Framework for Securing LLM-Powered Applications

The AI Security Frontier

Large Language Models and AI-powered applications have introduced an entirely new category of security risk. This whitepaper presents BugFoe's framework for identifying, assessing, and mitigating security risks in AI-powered systems based on research and assessments of 50+ LLM deployments.

Prompt Injection: The Dominant Risk

Prompt injection — manipulating an LLM's behavior by injecting adversarial instructions through user inputs, documents, or external data — is the most prevalent and high-impact AI security risk. Direct prompt injection attacks the model directly; indirect prompt injection embeds instructions in content the model processes (emails, documents, web pages).

AI Supply Chain Risks

Organizations deploying third-party models face supply chain risks including: model poisoning through training data contamination, malicious model weights distributed through public repositories, and insecure model serving infrastructure. Fine-tuned models using unvalidated datasets are particularly vulnerable.

Data Exposure Through AI

LLMs trained on organizational data or with access to internal systems can inadvertently expose sensitive information through model outputs. RAG (Retrieval Augmented Generation) architectures require careful access control design — the model should only retrieve and present data the requesting user is authorized to access.

The BugFoe AI Security Framework

Our framework assesses AI applications across five domains: (1) Input validation and prompt injection resistance, (2) Output filtering and sensitive data prevention, (3) Model access control and authentication, (4) Agentic action authorization and scope limitation, (5) Monitoring and anomaly detection for AI-specific attack patterns.

Recommendations

Implement prompt injection defenses through input sanitization, instruction hierarchy enforcement, and output monitoring. Apply least-privilege principles to AI agent capabilities. Maintain human-in-the-loop for high-risk actions. Conduct regular red team exercises specifically targeting AI components.