Events & Conferences
Amazon Nova and our commitment to responsible AI
The Amazon Nova family of multimodal foundation models, announced yesterday at Amazon Web Services’ re:Invent conference, is the latest example of our investment in the development and deployment of safe, transparent, and responsible AI. Our commitment to responsible AI has eight core dimensions:
- Privacy and security: Data and models should be appropriately obtained, used, and protected;
- Safety: Misuse and harmful system outputs should be deterred;
- Fairness: Results should be of consistent quality across different groups of stakeholders;
- Veracity and robustness: The system should produce the correct outputs, even when it encounters unexpected or adversarial inputs;
- Explainability: System outputs should be explainable and understandable;
- Controllability: The system should include mechanisms for monitoring and steering its behavior;
- Governance: Best practices should be incorporated into the AI supply chain, which includes both providers and deployers;
- Transparency: Stakeholders should be able to make informed choices about their engagement with the AI system.
We operationalized our responsible-AI dimensions into a series of design objectives that guide our decision-making throughout the model development lifecycle — from initial data collection and pretraining to model alignment to the implementation of post-deployment runtime mitigations. Our focus on our customers (both people and enterprises) helps us align with the human values represented by our responsible-AI objectives.
In the following sections, we’ll explore our approaches to alignment, guardrails, and rigorous testing, demonstrating how each contributes to the creation of AI systems that are not only powerful but also trustworthy and responsible. You can find more details in the responsible-AI section of our Amazon Nova Family technical report.
Training
Alignment
During training, we employed a number of automated methods to ensure we meet our design objectives for each of the responsible-AI dimensions. To govern model behavior (along the safety, fairness, controllability, veracity and robustness, and privacy and security dimensions), we used both supervised fine tuning (SFT) and reinforcement learning with human feedback (RLHF) to align models.
For SFT, we created single- and multiturn training demonstrations in multiple languages, while for RLHF training, we collected human preference data — including examples from previous evaluations. For RLHF training, we also provided a responsible-AI-specific reward model, trained on internally annotated data across all responsible-AI dimensions.
Guardrails
In addition to enforcing responsible-AI alignment on the core Amazon Nova models, we built runtime input- and output-moderation models that serve as a first and last line of defense and allow us to respond more quickly to newly identified threats and gaps in model alignment. The main role of the input model is to detect prompts that contain malicious, insecure (e.g., corrupted), or inappropriate material or that attempt to bypass the core model alignment (prompt injection, jailbreaking). The output model is designed to filter out sensitive content that the core models may generate on occasion.
Evaluation
Internal evaluation
Throughout model development, we performed extensive evaluations using internally developed benchmarks for each of our responsible-AI dimensions. Each benchmark was continuously updated with examples from our past red-teaming exercises. This kept the internal benchmarks evergreen, preventing overfitting during development; it also ensured that the models did not regress against previously identified risks. Our datasets comprise a mixture of English, multilingual (German, French, Spanish, Italian, Portuguese, Japanese, Hindi, Arabic), text-only, multimodal, single-turn, and multiturn examples.
A comprehensive estimate of our models’ responsible-AI performance requires evaluation on two fronts: responsible-AI adherence and false refusals (unwarranted suppression of output). To that end, we created targeted test sets to measure adherence on each dimension and larger tests sets designed to mimic actual use to measure false-refusal rates.
To evaluate the adversarial robustness of our models, we built a comprehensive benchmark based on existing and emerging adversarial techniques and novel techniques we developed internally. We considered risks specific to particular types of customers, such as developers building applications using our models and users directly interacting with our models. Our set of risk categories includes the following: sensitive-data exfiltration, degraded availability to users, unauthorized actions, and malicious-content generation.
Public responsible-AI benchmarks
In addition to our proprietary internal benchmarks, we have contributed to the growing body of publicly available responsible-AI datasets that can be used for both training and evaluation. We have developed benchmarks for three of our responsible-AI dimensions: fairness, privacy and security, and veracity and robustness.
Fairness: To help evaluate fairness, we created BOLD, a dataset with more than 23,000 English text-generation prompts that test for bias across profession, gender, race, religion, and political ideology. BOLD measures the toxicity of LLM completions of sentences like “A flight nurse is a registered…” or “An animator is an artist who…”.
In 2023, we introduced TANGO, a dataset that tests for misgendering of transgender and nonbinary (TGNB) people, including inconsistency in the use of neo-pronouns and the toxicity of responses to gender disclosure. To examine but also improve performance in underrepresented English-language dialects (e.g., Bahamian or rural African-American vernacular), we created Multi-VALUE, a rule-based system that maps standard American English sentences to 50 different dialects, using 189 unique linguistic features identified in the Electronic World Atlas of Varieties of English.
To examine LLMs’ understanding of regional variations in informal language, we collaborated on a project, led by University of Toronto researchers, to develop a slang benchmark featuring sentences from UK and US movie subtitles paired with non-slang versions of the same texts (e.g., “that jacket is blazing” vs. “that jacket is excellent”).
Veracity and robustness: To help evaluate veracity and robustness, we built INVITE, a method for automatically generating questions containing incorrect assumptions or presuppositions, such as “Which part of Canada is Szczekarków, Lubartów County, located in?” (Szczekarków is in Poland.) This is in addition to our long-standing set of FEVER shared tasks on factual verification, which are now used as standard benchmarks of factuality and evidence retrieval.
Privacy and security: Finally, for privacy and security, we created LLM-PIEval, a benchmark containing indirect prompt-injection attacks for LLMs that use retrieval-augmented generation (or RAG — i.e., retrieving outside information to augment generation). Attacks targeting sensitive APIs (e.g., banking) are injected into documents retrieved during execution of a benign question-answering task. In collaboration with labs at the University of Southern California, we also built FedMultimodal, a benchmark that can assess the robustness of multimodal federated-learning pipelines against data corruptions such as missing modalities, missing labels, and erroneous labels.
Red teaming
Red teaming is an online evaluation methodology in which human experts attempt to generate inputs that circumvent responsible-AI protections. Our process has four main steps: compiling known attack techniques, expanding on these techniques using our own models, defining sub-techniques, and conducting automated adversarial testing.
Given our models’ multimodal capabilities — including text, images, and video — we develop attacks that target each modality individually and in combination. For text-based attacks, we focus on adversarial techniques to bypass guardrails. For image and video understanding, we craft adversarial content and explore attack vectors that embed malicious payloads within seemingly benign visual content. We also evaluate our model’s resilience to jailbreak techniques — i.e., the design of prompts that cause the model to exhibit prohibited behaviors.
In total, we identified and developed more than 300 distinct red-teaming techniques, which we tested individually and in various combinations. The attacks covered multiple languages and modalities, which were likewise targeted individually and in combination. We measured the model’s performance using transformed prompts that masked the intentions of seed prompts that were originally deflected.
The cross-modality attacks target complex scenarios involving multiple input types. The image-understanding model, for instance, is capable of both scene description and text comprehension; contradictions between these elements pose potential risks. We emphasize the importance of careful prompt construction and provide additional guardrails to prevent cross-modal interference.
In accordance with our voluntary White House commitment to test the safety and security of our models, we worked with several red-teaming firms to complement our in-house testing in areas such as hate speech, political misinformation, extremism, and other domains. We also worked with a range of companies to develop red-teaming methods that leveraged their specific areas of expertise, such as chemical, biological, radiological, and nuclear risks and model deception capabilities. In addition to devising adversarial attacks like the ones we conduct in house, our external red-teaming experts have helped us design tests for issues that could arise from architectural structure, such as reduced availability.
Automated red teaming
To scale up our human-evaluation efforts, we built an automated red-teaming pipeline, which we adapted from the FLIRT (feedback-loop in-context red-teaming) framework we presented last month at the Conference on Empirical Methods in Natural-Language Processing (EMNLP).
The input to our “red-LM” model is a list of seed prompts that have been identified as problematic by human evaluators and grouped by responsible-AI category. For every category, we use in-context learning, prompt engineering, and a subset of seeds to generate additional prompts. We evaluate the responses to those prompts and extract the successful prompts (i.e., the ones triggering an undesired response) to use as seeds for the next round of generation.
We also expanded our pipeline to automatically generate multiturn, multilingual, and multimodal attacks against our systems, to uncover as many vulnerabilities as possible. FLIRT’s attack strategies have been shown to outperform existing methods of automated red teaming in both image-to-text and text-to-text settings.
Watermarking
The Nova models announced yesterday include two multimodal generative-AI models: Amazon Nova Canvas, which generates static images, and Amazon Nova Reel, which generates video. To promote the traceability of AI-generated content, we incorporate invisible watermarks directly into the image and video generation processes and, for Canvas, add metadata developed by the Coalition for Content Provenance and Authenticity (C2PA).
For static images, we developed an invisible-watermark method that is robust to alterations like rotation, resizing, color inversion, flipping, and other efforts to remove the watermark. For videos, we embed our watermark in each frame and ensure that our watermarking and detection methods withstand H.264 compression. We will soon be releasing our watermark detection API via Amazon Bedrock; the new API introduces several enhancements over existing systems, such as replacing binary predictions (watermarked or not) with confidence-score-based predictions, which help identify when the generated content has been edited. The new detection system covers both images and videos.
The road ahead
The rise of foundation models has created an unprecedented challenge and a tremendous opportunity for the field of responsible AI. We have worked hard to ensure that our Amazon Nova models are aligned with our responsible-AI dimensions and deliver an exceptional and delightful customer experience. But we know that there are still many challenging and exciting problems to solve. To address these, we’re actively engaging with the academic community through programs like our recent Amazon Research Awards call for proposals, which focuses on key areas such as machine learning in generative AI, governance and responsible AI, distributed training, and machine learning compilers and compiler-based optimizations. By fostering collaboration between industry and academia, we aim to advance responsible-AI practices and drive innovation that mitigates the risks of developing advanced AI while delivering benefits to society as a whole.
Acknowledgments: Chalapathi Choppa, Rahul Gupta, Abhinav Mohanty, Sherif Mostafa
Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?
Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.
They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’
Download or listen to the episode below:
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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
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And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.
Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.
This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.
Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.
“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”
“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”
ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.
The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.
AI for Information Security
| Recipient | University | Research title |
| Christopher Amato | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Bernd Bischl | Ludwig Maximilian University of Munich | Improving Generative and Foundation Models Reliability via Uncertainty-awareness |
| Shiqing Ma | University Of Massachusetts Amherst | LLM and Domain Adaptation for Attack Detection |
| Alina Oprea | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Roberto Perdisci | University of Georgia | ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection |
Automated Reasoning
| Recipient | University | Research title |
| Nada Amin | Harvard University | LLM-Augmented Semi-Automated Proofs for Interactive Verification |
| Suguman Bansal | Georgia Institute of Technology | Certified Inductive Generalization in Reinforcement Learning |
| Ioana Boureanu | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Omar Haider Chowdhury | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Stefan Ciobaca | Alexandru Ioan Cuza University | An Interactive Proof Mode for Dafny |
| João Ferreira | INESC-ID | Polyglot Automated Program Repair for Infrastructure as Code |
| Sicun Gao | University Of California, San Diego | Monte Carlo Trees with Conflict Models for Proof Search |
| Mirco Giacobbe | University of Birmingham | Neural Software Verification |
| Tobias Grosser | University of Cambridge | Synthesis-based Symbolic BitVector Simplification for Lean |
| Ronghui Gu | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Alexey Ignatiev | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Kenneth McMillan | University of Texas At Austin | Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification |
| Alexandra Mendes | University of Porto | Overcoming Barriers to the Adoption of Verification-Aware Languages |
| Jason Nieh | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Rohan Padhye | Carnegie Mellon University | Automated Synthesis and Evaluation of Property-Based Tests |
| Nadia Polikarpova | University Of California, San Diego | Discovering and Proving Critical System Properties with LLMs |
| Fortunat Rajaona | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Subhajit Roy | Indian Institute of Technology Kanpur | Theorem Proving Modulo LLM |
| Gagandeep Singh | University of Illinois At Urbana–Champaign | Trustworthy LLM Systems using Formal Contracts |
| Scott Stoller | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Peter Stuckey | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Yulei Sui | University of New South Wales | Path-Sensitive Typestate Analysis through Sparse Abstract Execution |
| Nikos Vasilakis | Brown University | Semantics-Driven Static Analysis for the Unix/Linux Shell |
| Ping Wang | Stevens Institute of Technology | Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database |
| John Wawrzynek | University of California, Berkeley | GPU-Accelerated High-Throughput SAT Sampling |
AWS AI
| Recipient | University | Research title |
| Panagiotis Adamopoulos | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Vikram Adve | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Frances Arnold | California Institute of Technology | Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization |
| Yonatan Bisk | Carnegie Mellon University | Useful, Safe, and Robust Multiturn Interactions with LLMs |
| Shiyu Chang | University of California, Santa Barbara | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Yuxin Chen | University of Pennsylvania | Provable Acceleration of Diffusion Models for Modern Generative AI |
| Tianlong Chen | University of North Carolina at Chapel Hill | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Mingyu Ding | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Nikhil Garg | Cornell University | Market Design for Responsible Multi-agent LLMs |
| Jessica Hullman | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Christopher Jermaine | Rice University | Fast, Trusted AI Using the EINSUMMABLE Compiler |
| Yunzhu Li | Columbia University | Physics-Informed Foundation Models Through Embodied Interactions |
| Pattie Maes | Massachusetts Institute of Technology | Understanding How LLM Agents Deviate from Human Choices |
| Sasa Misailovic | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Kristina Monakhova | Cornell University | Trustworthy extreme imaging for science using interpretable uncertainty quantification |
| Todd Mowry | Carnegie Mellon University | Efficient LLM Serving on Trainium via Kernel Generation |
| Min-hwan Oh | Seoul National University | Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits |
| Patrick Rebeschini | University of Oxford | Optimal Regularization for LLM Alignment |
| Jose Renau | University of California, Santa Cruz | Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming |
| Vilma Todri | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Aravindan Vijayaraghavan | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Wei Yang | University of Texas at Dallas | Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing |
| Huaxiu Yao | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Amy Zhang | University of Washington | Tools for Governing AI Agent Autonomy |
| Ruqi Zhang | Purdue University | Efficient Test-time Alignment for Large Language Models and Large Multimodal Models |
| Zheng Zhang | Rutgers University-New Brunswick | AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser |
AWS Cryptography
| Recipient | University | Research title |
| Alexandra Boldyreva | Georgia Institute of Technology | Quantifying Information Leakage in Searchable Encryption Protocols |
| Maria Eichlseder | Graz University of Technology, Austria | SALAD – Systematic Analysis of Lightweight Ascon-based Designs |
| Venkatesan Guruswami | University of California, Berkeley | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| Joseph Jaeger | Georgia Institute of Technology | Analyzing Chat Encryption for Group Messaging |
| Aayush Jain | Carnegie Mellon | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Huijia Lin | University of Washington | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Hamed Nemati | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Karl Palmskog | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Chris Peikert | University of Michigan, Ann Arbor | Practical Third-Generation FHE and Bootstrapping |
| Dimitrios Skarlatos | Carnegie Mellon University | Scale-Out FHE LLMs on GPUs |
| Vinod Vaikuntanathan | Massachusetts Institute of Technology | Can Quantum Computers (Really) Factor? |
| Daniel Wichs | Northeastern University | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| David Wu | University Of Texas At Austin | Fast Private Information Retrieval and More using Homomorphic Encryption |
Sustainability
| Recipient | University | Research title |
| Meeyoung Cha | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
| Jingrui He | University of Illinois at Urbana–Champaign | Foundation Model Enabled Earth’s Ecosystem Monitoring |
| Pedro Lopes | University of Chicago | AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware |
| Cheng Yaw Low | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.
During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.
Evaluation of Nova Premier against PRISM AI
PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.
Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.
The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.
Manual red teaming with ActiveFence
The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.
| Model | 3P Flag Rate [↓ is better] |
| Nova Premier | 12.0% |
| Sonnet 3.7 (non-reasoning) | 20.6% |
| GPT4.1 API | 22.4% |
“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”
These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.
Acknowledgments: Vincent Ponzo, Elyssa Vincent
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