Adversarial AI Defense in Large Language Models & Generative AI

Abstract

The rapid growth of Large Language Models (LLMs) and generative AI models has enabled a suite of novel capabilities, including advanced analysis of natural language, code synthesis, creative content creation, and multimodal reasoning. Yet, this power brings with it substantial vulnerability to adversarial control. Unlike narrow models of traditional machine learning, generative AI models operate in the open with data from many different sources, other data sources and autonomous applications, and the adversarial surface is increased from text to image/multimodal. New threats appear in three main guises: immediate injection, where malign instructions overwhelm or hijack the running of tools even in safe environments; adversarial examples, where crafted perturbations of inputs corrupt decoding paths to produce harmful, biased, or simply incorrect outputs; and data poisoning, where perturbation of training, fine-tuning, or retrieval corpora introduces latent backdoors, obliterates alignment, or destroys factuality at scale.

This work aims to respond to the pressing need for a defense-in-depth model that balances robustness and usability, as no single defense of those measures is adequate. We amalgamate recent developments from adversarial machine learning, robust training, secure system design and AI risk governance to outline a multi-layer approach for adversarial resilience in LLMs and more generally in generative AI. On the model side, we investigate adversarial training with red-teamed artifacts, constitutional alignment, safety-aware decoding as well as the use of guard models including Llama Guard in text and multimodal setting. At the data level, we emphasize deduplication, provenance, and the role of standards such as C2PA for verifiable authenticity, and poisoning-resilient corpus curation. The application layer offers, on demand, trust boundaries on retrieval-augmented generation (RAG), sandboxed tool use, schema enforcement, and indirect-injection-detection prompts. Finally, at the governance and operational tiers, we focus on benchmarks like Jailbreak Bench and Agent Harm, embedding red-teaming into CI/CD pipelines, applying NIST’s Generative AI Profile, and continuous telemetry-driven monitoring with incident disclosure.

The proposed approach, DiD-GEN (Defense-in-Depth for Generative AI) organizes these governors into a repeatable, standards-aligned implementation for the enterprise, research, and regulatory users. Evaluation over recent benchmarks shows that adversarial success rates can never be wiped out, however, layered defect-oriented defenses do incur noticeable decreases in exploitability while retaining task utility. This paper concludes by proposing concrete paths forward for organizations aiming to harden generative AI deployments against adaptive adversaries, emphasizing resilience, accountability, and compliance toward publication-quality robustness obligations