arXiv subjects

Anusha Sinha

Publications and source records attributed to Anusha Sinha.

5 recordsLinked to original sources

arXiv2026

On the Framing Effect of AI Red-Teaming Instructions: Evidence from Prior Exercises and Controlled Simulations

AI red-teaming, adversarially probing generative AI systems to surface harms, has become a central evaluation method. However, prior work has highlighted that red-teaming is a fairly open-ended task, laden with myriad design choices that can shape its findings. In this work, we focus on the instructions used to conduct red-teaming exercises and hypothesize that they can significantly affect outcomes in a manner analogous to the framing effect in psychology, where the format in which information is presented greatly impacts how people respond. To understand whether and why instructions impact red-teaming outcomes, first, we analyze the range of ways in which instructions have historically been formulated, reviewing 228 instances of prior work. Second, based on takeaways from this review, we run controlled simulations that hold parameters such as the risk domain, the attacker, and the model under investigation fixed while varying only the instructions. From five real instruction templates used by industry and academic organizations, we derive 45 instruction frames across three risk domains and three levels of risk granularity, generating 100 variants per frame, and deploy each against five open-weights target models, for 22,500 attacks. Using a generalized linear mixed model, we find that certain instruction properties significantly predict attack success rates: the lowest and highest levels of risk granularity both outperform intermediate specificity, as do short and long instructions relative to medium-length ones. The presence of examples is associated with higher attack success rates, while supplying explicit harm definitions yields lower ones. As alignment evaluations become increasingly automated, our results suggest that humans continue to shape red-teaming outcomes substantially through the instructions they provide to scaffold the activity.

cs.CY↗

arXiv2026

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data

We study the efficacy of fine-tuning Large Language Models (LLMs) for the specific task of report (government archives, news, intelligence reports) summarization. While this topic is being very actively researched - our specific application set-up faces two challenges: (i) ground-truth summaries maybe unavailable (e.g., for government archives), and (ii) availability of limited compute power - the sensitive nature of the application requires that computation is performed on-premise and for most of our experiments we use one or two A100 GPU cards. Under this set-up we conduct experiments to answer the following questions. First, given that fine-tuning the LLMs can be resource intensive, is it feasible to fine-tune them for improved report summarization capabilities on-premise? Second, what are the metrics we could leverage to assess the quality of these summaries? We conduct experiments on two different fine-tuning approaches in parallel and our findings reveal interesting trends regarding the utility of fine-tuning LLMs. Specifically, we find that in many cases, fine-tuning helps improve summary quality and in other cases it helps by reducing the number of invalid or garbage summaries.

cs.CL↗

arXiv2025

From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming

A red team simulates adversary attacks to help defenders find effective strategies to defend their systems in a real-world operational setting. As more enterprise systems adopt AI, red-teaming will need to evolve to address the unique vulnerabilities and risks posed by AI systems. We take the position that AI systems can be more effectively red-teamed if AI red-teaming is recognized as a domain-specific evolution of cyber red-teaming. Specifically, we argue that existing Cyber Red Teams who adopt this framing will be able to better evaluate systems with AI components by recognizing that AI poses new risks, has new failure modes to exploit, and often contains unpatchable bugs that re-prioritize disclosure and mitigation strategies. Similarly, adopting a cybersecurity framing will allow existing AI Red Teams to leverage a well-tested structure to emulate realistic adversaries, promote mutual accountability with formal rules of engagement, and provide a pattern to mature the tooling necessary for repeatable, scalable engagements. In these ways, the merging of AI and Cyber Red Teams will create a robust security ecosystem and best position the community to adapt to the rapidly changing threat landscape.

cs.LG↗

arXiv2024

Red-Teaming for Generative AI: Silver Bullet or Security Theater?

In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming's central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices. Our analysis reveals that prior methods and practices of AI red-teaming diverge along several axes, including the purpose of the activity (which is often vague), the artifact under evaluation, the setting in which the activity is conducted (e.g., actors, resources, and methods), and the resulting decisions it informs (e.g., reporting, disclosure, and mitigation). In light of our findings, we argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, and that industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI, gestures towards red-teaming (based on public definitions) as a panacea for every possible risk verge on security theater. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.

cs.CY↗

arXiv2022

Measuring AI Systems Beyond Accuracy

Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the other phases of the ML system lifecycle. Research investigating cross-domain approaches to ML T&E is needed to drive the state of the art forward and to build an Artificial Intelligence (AI) engineering discipline. This paper advocates for a robust, integrated approach to testing by outlining six key questions for guiding a holistic T&E strategy.

cs.SE↗