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How Project P.I. helps Amazon remove imperfect products
Although there are hundreds of millions of products stored in Amazon fulfillment centers, it’s very rare for customers to report shipped products as damaged. However, Amazon’s culture of customer obsession means that teams are actively working to find and remove even that relatively small number of imperfect products before they’re delivered to customers.
One of those teams includes scientists who are using generative AI and computer vision, powered by AWS services such as Amazon Bedrock and Amazon SageMaker, to help spot, isolate, and remove imperfect items.
Inside Amazon fulfillment centers across North America, products ranging from dog food and phone cases to T-shirts and books pass through imaging tunnels for a wide variety of uses, including sorting products based on their intended destination. Those use cases have been extended to include the use of artificial intelligence to inspect individual items for defects.
For example, optical character recognition (OCR) — the process that converts an image of text into a machine-readable text format — checks expiration dates on product packaging to ensure expired items are not sent to customers. Computer vision (CV) models — trained with reference images from the product catalog and actual images of products sent to customers — pore over color and monochrome images for signs of product damage such as bent book covers.
Additionally, a recent breakthrough solution leverages the ability of generative AI to process multimodal information by synthesizing evidence from images captured during the Amazon fulfillment process and combining it with written customer feedback to trigger even faster corrective actions.
This effort, referred to collectively as Project P.I., which stands for “private investigator”, encompasses the team’s vision of using a detective-like toolset to uncover both defects and, wherever possible, their cause — to address the issue at its root before a product reaches the customer.
“We want to equip ourselves with the most powerful, scalable tools and levers to help us protect our customers’ trust,” said Pingping Shan, director of perfect order experience at Amazon.
Defect detection
Project P.I. is an outgrowth of Amazon’s product quality program, and the tools and systems developed by the team’s scientists include machine learning models that assist selling partners with listing products with accurate information.
“The product quality team is constantly looking for ways to both reduce the burden on the sellers and to proactively verify the condition of inventory in fulfillment centers,” Shan said.
An early solution was an OCR model that checks the labeling information when inventory arrives and compares that to the information in Amazon’s database. If a mismatches occurs — such as a pallet of dog food with an earlier sell-by date than the date in the database — the team can isolate and inspect the pallet and prevent any expired products from reaching the customer.
When an item-level defect is detected, Amazon takes several steps to resolve the issue, including investigating whether the item is one in a defective batch and, if so, isolating the batch from the rest of the items, explained Angela Ke, a senior product manager.
“We want to make sure that customers don’t have to experience issues with product quality. That’s really the vision of Project P.I.,” she said. “We want to get it right for customers the first time, so we want to inspect the products before they leave our fulfillment center, and we incorporate AI to streamline the workflow.”
Customer feedback aids model training
Despite the team’s best efforts, sometimes product quality issues only become known after an item has been delivered to customers, noted Mark Ma, a principal product manager. Those arise in cases where customers have filed a return noting the issue. In those instances, the team tracks down the batch the product came from, verifies the issue, removes those items from fulfillment center shelves, issues refunds, and communicates the issue to the seller.
“We know that that correcting the defects after they happen is not the best way to protect and improve the customer experience. That’s why we started exploring what kind of data we can gather further upstream,” he said. Those discussions eventually led to leveraging the tunnel images to better identify products with defects and take surgical and proactive action to address them — before they’re packaged and shipped.
One of the early challenges with that approach entailed training CV models to correctly identify defects, noted Vincent Gao, a senior science manager on the product quality team.
“It’s like finding a needle in a haystack,” he said. “We needed a model that could accurately identify those among all the other normal products. Otherwise, we could be finding a lot of false positives making the fulfillment process inefficient.”
Gao’s team turned to an ensemble approach that combines self-supervised models with supervised transformer models —a neural-network architecture that uses attention mechanisms to improve performance on machine learning tasks — to spot the difference between normal and defective items. By learning what the “correct” product looks like from fulfillment center images associated with normal orders, the model can compare an item on its way to be packaged against its “normal” image and provide a measurement of how much it differs.
This approach allowed the team to more reliably spot obvious product defects, such as a book with a torn cover or an empty canister of tennis balls, yet it still couldn’t account for some of the fine grain details like a mislabeled T-shirt size or bent box.
To achieve that, the team turned to customer feedback to help train a variety of ML models that can spot the difference between normal and defective items. This more detailed, labeled data was used to refine the model to detect the types of defects customers notice.
“Using that, we are able to be more targeted on the areas that we want to identify so that we can enable the models to learn more on those finer details,” Gao said.
Leveraging generative AI
Today, the science team is leveraging breakthroughs in generative AI to make product defect detection more scalable and robust. For example, the team launched a multimodal large language model (MLLM) that’s been trained to identify damage such as broken seals, torn boxes, and bent book covers, and report in plain language the damage it detects.
The LLM is working side-by-side with the visual language model to analyze data from different sources and modalities to help us make a decision.
“We use the MLLM to ingest and understand the images from fulfillment centers to identify damage patters with zero-shot learning capability — meaning the model can recognize something it has not seen in training. That is a significant plus when it comes to identifying damage patterns given their vast variation,” Ma explained. “Then we use the model to summarize common damage patterns, which enable us to work more upstream with our selling partners and manufactures to proactively address these issues.”
With traditional CV technologies, a model would be trained for each damage scenario – broken seal, torn box, etc. – Gao said, resulting in an unscalable ensemble of dozens to hundreds of models. The MLLM, on the other hand, is a single and scalable unified solution.
“That’s the new power we now have on top of the classic computer vision,” Shan said.
The Project P.I. team has also recently put into production a generative AI system that uses an MLLM to investigate the root cause of negative customer experiences. The system first reviews customer feedback about the issue and then analyzes product images collected by the tunnels and other data sources to confirm the root cause.
For example, if a customer contacts Amazon because they ordered twin-size sheets but received king-size, the generative AI system cross-references that feedback with fulfillment center images. The system will ask questions such as, “Is the product label visible in the image?” “Does the label read king or twin?”
The system’s vision-language model in turn looks at the images, extracts the text from the label, and answers the questions. The LLM converts the answers into a plainspoken summary of the investigation.
“The LLM is working side-by-side with the visual language model to analyze data from different sources and modalities to help us make a decision,” said Gao. “We can actually have the LLM trigger the vision-language model to finish all the different verification tasks.”
Proof of concept in the fulfillment center
Since May 2022, the product quality team has been rolling out their item-level product defect detection solutions using imaging tunnels at several fulfillment centers in North America.
The results have been promising. The system has proven itself adept at sorting through the millions of items that pass through the tunnels each month and accurately identifying both expired items and issues such as wrong color or size.
In the future, the team aims to implement near real-time product defect detection with local image processing. In this scenario, defective items could be pulled off the conveyor belt and a replacement item automatically ordered, thus eliminating disruptions to the fulfillment process.
“Ultimately, we want to be behind the scenes. We don’t need our customers to know this is going on,” said Keiko Akashi, a senior manager of product management at Amazon. “The customer should be getting a perfect order and not even know that the expired or damaged item existed.”
Sidelining defective items will also result in fewer returns, which has an added sustainability benefit, noted Gao.
“We want to intercept the wrong items or defective items,” he said. “That translates to less back and forth shipping overhead, while also delivering a better customer experience.”
New avenues for investigation
Seamless integration of these solutions across the Amazon fulfillment center network will require refinements to the AI models such as the ability to parse a potential misperception of a defect from an actual defect. For example, a “manufactured on” date might be conflated with an “expiration” date or sneakers that arrive without a shoebox are the wrong item instead of a step to reduce packaging, noted Ke.
What’s more, there are challenges adapting CV models to the unique nuances of each fulfillment center and region, such as the size and color of the totes used to convey items around fulfillment centers, and the ability to extract data across a multitude of languages.
“There’s a lot of information that’s written in words,” Ke explained. “So how do we make sure that the model is picking up the right language and translating it correctly? That’s another challenge our science team is trying to solve.”
As the team has gone down this road, they’ve amassed data that shows the defects sometimes are the result of what happens outside of Amazon’s fulfillment centers.
“It could have been a carrier issue,” noted Akashi. “When customers say, ‘Hey, it came damaged,’ we can look into our outbound images and see that nothing has gone wrong. Then we can go figure out what else is going on.”
The team also plans to make data on defects more easily accessible to selling partners, Akashi added. For example, if Amazon discovered a seller accidentally put stickers with the wrong size on a product, Amazon would communicate the issue to help prevent the error from happening again.
“There’s an opportunity to get this information in front of our selling partners so they have visibility to their own inventory, and they can also have more succinct root causes to why these returns are happening,” she explained. “We’re excited that the data that we’re gathering and the AI models we are creating will benefit our customers and selling partners.”
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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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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