As large language models (LLMs) and generative AI become widely adopted, guardrails have emerged as a key tool to ensure their safe use. However, adding guardrails isn't without tradeoffs; stronger security measures can reduce usability, while more flexible systems may leave gaps for adversarial attacks. In this work, we explore whether current guardrails effectively prevent misuse while maintaining practical utility. We introduce a framework to evaluate these tradeoffs, measuring how different guardrails balance risk, security, and usability, and build an efficient guardrail. Our findings confirm that there is no free lunch with guardrails; strengthening security often comes at the cost of usability. To address this, we propose a blueprint for designing better guardrails that minimize risk while maintaining usability. We evaluate various industry guardrails, including Azure Content Safety, Bedrock Guardrails, OpenAI's Moderation API, Guardrails AI, Nemo Guardrails, and Enkrypt AI guardrails. Additionally, we assess how LLMs like GPT-4o, Gemini 2.0-Flash, Claude 3.5-Sonnet, and Mistral Large-Latest respond under different system prompts, including simple prompts, detailed prompts, and detailed prompts with chain-of-thought (CoT) reasoning. Our study provides a clear comparison of how different guardrails perform, highlighting the challenges in balancing security and usability.

Large Language Models (LLMs) are being adopted across a wide range of tasks, including decision-making processes in industries where bias in AI systems is a significant concern. Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations. Building upon the frameworks of the LLM Implicit Association Test (IAT) Bias and LLM Decision Bias, this study highlights that newer or larger language models do not automatically exhibit reduced bias; in some cases, they displayed higher bias scores than their predecessors, such as in Meta's Llama series and OpenAI's GPT models. This suggests that increasing model complexity without deliberate bias mitigation strategies can unintentionally amplify existing biases. The variability in bias scores within and across providers underscores the need for standardized evaluation metrics and benchmarks for bias assessment. The lack of consistency indicates that bias mitigation is not yet a universally prioritized goal in model development, which can lead to unfair or discriminatory outcomes. By broadening the detection of implicit bias, this research provides a more comprehensive understanding of the biases present in advanced models and underscores the critical importance of addressing these issues to ensure the development of fair and responsible AI systems.

Large language models (LLMs) exhibit remarkable capabilities but often produce inaccurate responses, as they rely solely on their embedded knowledge. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating an external information retrieval system, supplying additional context along with the query to mitigate inaccuracies for a particular context. However, accuracy issues still remain, as the model may rely on irrelevant documents or extrapolate incorrectly from its training knowledge. To assess and improve the performance of both the retrieval system and the LLM in a RAG framework, we propose \textbf{VERA} (\textbf{V}alidation and \textbf{E}nhancement for \textbf{R}etrieval \textbf{A}ugmented systems), a system designed to: 1) Evaluate and enhance the retrieved context before response generation, and 2) Evaluate and refine the LLM-generated response to ensure precision and minimize errors. VERA employs an evaluator-cum-enhancer LLM that first checks if external retrieval is necessary, evaluates the relevance and redundancy of the retrieved context, and refines it to eliminate non-essential information. Post-response generation, VERA splits the response into atomic statements, assesses their relevance to the query, and ensures adherence to the context. Our experiments demonstrate VERA's remarkable efficacy not only in improving the performance of smaller open-source models, but also larger state-of-the art models. These enhancements underscore VERA's potential to produce accurate and relevant responses, advancing the state-of-the-art in retrieval-augmented language modeling. VERA's robust methodology, combining multiple evaluation and refinement steps, effectively mitigates hallucinations and improves retrieval and response processes, making it a valuable tool for applications demanding high accuracy and reliability in information generation. .

Large Language Models (LLMs) have become very popular and have found use cases in many domains, such as chatbots, auto-task completion agents, and much more. However, LLMs are vulnerable to different types of attacks, such as jailbreaking, prompt injection attacks, and privacy leakage attacks. Foundational LLMs undergo adversarial and alignment training to learn not to generate malicious and toxic content. For specialized use cases, these foundational LLMs are subjected to fine-tuning or quantization for better performance and efficiency. We examine the impact of downstream tasks such as fine-tuning and quantization on LLM vulnerability. We test foundation models like Mistral, Llama, MosaicML, and their fine-tuned versions. Our research shows that fine-tuning and quantization reduces jailbreak resistance significantly, leading to increased LLM vulnerabilities. Finally, we demonstrate the utility of external guardrails in reducing LLM vulnerabilities.