AI Security Services

AI security services address the unique security challenges of organizations that develop, deploy, or depend on artificial intelligence systems. As AI models become central to business processes — from fraud detection to clinical decision support to autonomous customer interactions — their security properties become critical. AI security encompasses adversarial machine learning, model integrity, training data security, inference API security, and the new class of vulnerabilities introduced by large language models (LLMs) including prompt injection, jailbreaking, and data exfiltration through model outputs. BugFoe's AI security team combines deep expertise in both offensive security and machine learning to provide comprehensive assessments of AI systems.

The OWASP Top 10 for Large Language Model Applications catalogs the most critical security risks facing organizations deploying LLMs: prompt injection — where malicious inputs manipulate the model to ignore its instructions and perform unauthorized actions — is particularly dangerous because it cannot be reliably prevented by input filtering alone. Insecure output handling allows LLM outputs to trigger secondary attacks such as XSS, SQL injection, or SSRF when they are rendered or processed without sanitization. Training data poisoning and model theft represent threats to the AI systems themselves rather than to the data they process. As LLMs are integrated into agentic workflows with access to external tools, file systems, and APIs, the attack surface expands dramatically.

BugFoe's AI security assessments cover the full lifecycle of AI system development and deployment. For LLM-powered applications, we conduct red team exercises to identify prompt injection vulnerabilities, test system prompt confidentiality, evaluate guardrail robustness, and assess the security of any tools or APIs the LLM can invoke. For traditional ML systems, we evaluate training data integrity, model robustness against adversarial examples, and inference API security. For organizations developing RAG (Retrieval-Augmented Generation) architectures, we specifically test the security of the knowledge base, the retrieval mechanism, and the generation pipeline for data exfiltration risks. All findings are accompanied by implementation guidance that security teams can act on immediately.