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Text style transfer in enterprise environments presents unique challenges that extend beyond traditional style transfer approaches, particularly when dealing with complex technical documentation and strict organizational guidelines. This paper introduces Onoma, a novel enterprise-scale style transfer system that addresses the fundamental challenges of maintaining consistent brand voice while preserving

AES-GCM has seen great adoption for the last 20 years to protect data in various use-cases because of its optimal performance. It has also posed some challenges to modern applications due to its nonce, block size, and lack of key commitment. Nonce-derived schemes address these challenges by deriving a different key from random values and using GCM with the derived key. In this work, we explore efficient

The results of software verification are only as trustworthy as the provided specification. Errors or incompleteness in the specification can result in unwarranted confidence in the implementation. Previous work, particularly in the realm of model checking, has investigated a notion of coverage in verification. Portions of the system that could be replaced arbitrarily without causing verification failure

In this paper we investigate cross-lingual Text-To-Speech (TTS) synthesis through the lens of adapters, in the context of lightweight TTS systems. In particular, we compare the tasks of unseen speaker and language adaptation with the goal of synthesising a target voice in a target language, in which the target voice has no recordings therein. Results from objective evaluations demonstrate the effectiveness

From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.

Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow allows media researchers and analysts to query relevant historical plays using natural language, rather than using traditional filter and click-based interfaces. The agentic workflow takes a user query in natural

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Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks in recent years. While prior work has explored leveraging LLMs to generate synthetic data for self-improvement, repeated iterations often suffer from diminishing returns due to the reliance on homogeneous reasoning patterns and limited exploration of alternative perspectives. In this paper, we introduce a

AI systems for software development are rapidly gaining prominence, yet significant challenges remain in ensuring their safety. To address this, Amazon launched the Trusted AI track of the Amazon Nova AI Challenge, a global competition among 10 university teams to drive advances in secure AI. In the challenge, five teams focus on developing automated red teaming bots, while the other five create safe AI

WARNING: Contains harmful content that can be offensive in nature Large language models (LLMs) for code generation have enhanced developer productivity while introducing new misuse vectors, as these models can generate potentially harmful code. Existing evaluation methods fail to assess such misuse scenarios, and structured red teaming pipelines for code generation remain under-developed. This paper presents

We introduce AlquistCoder, a code-generating system that effectively minimizes the risk of producing malicious content or vulnerable code while maintaining excellent Python coding and question answering standards across a wide range of tasks. The architecture of AlquistCoder employs a sophisticated input guardrail classifier that analyzes whether the user’s intention is benign, potentially harmful, or falls

We present SecureLion, a trustworthy AI assistant designed to securely han-dle cybersecurity queries and generate vulnerability-free code. Compared to the base model, our system achieves a 76.6% relative reduction on insecure messages in an adversarial competition setting at the cost of negligible utility loss. The success of SecureLion stems primarily from: (1) pervasive security-focused reasoning integrated

While large language models (LLMs) achieve strong performance in code generation, persistent security vulnerabilities hinder their safe deployment. Starting from a pretrained CodeGen model without inherent safety mechanisms, we develop a systematic synthetic instruction-tuning workflow to progressively enhance model security. Our pipeline begins with taxonomy-guided synthetic data, capturing diverse attack

Warning: This report contains partially redacted content that may be offensive to the reader Large language models (LLMs) may assist users with malicious cybersecurity at-tacks or inadvertently generate code with critical security flaws. These failures stem from their broader inability to reliably identify safe data or generate safe outputs, despite advances in alignment research. We identify three potential

AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain—especially in high-stakes domains like cybersecurity. Current red-teaming tools often rely on fixed bench-marks or unrealistic prompts, missing many real-world vulnerabilities. We present ASTRA, an automated agent system designed to systematically uncover safety flaws in AI-driven

We introduce PurpCode, a novel post-training method that aligns coding assistants to perform safety reasoning to defend against malicious cyber activities and provide secure and functional code. Our approach trains a reasoning model in two stages:(i) Rule learning, which explicitly teaches the model to reference cyber safety rules to avoid facilitating malicious cyber events and to generate vulnerability-free

Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studies have shown that these models are prone to gen-erating vulnerable or even malicious code under adversarial settings. Existing red-teaming approaches rely on extensive human effort, limiting their scalability and practicality,

This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models (LLMs) in AI-assisted software development. Our work is driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbreaks; (2) a diverse generative multi-turn attack

In this technical report, we present our automated red-teaming framework designed to induce jailbreaks in targeted code-generating Large Language Models (LLMs), prompting them to generate malicious and vulnerable code. As of May 13, according to the latest competition leaderboard, our solution has achieved top performance in the second tournament. Our solution consists of three primary modules. First, we