[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81825-en":3,"doc-seo-81825-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81825,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","LIB-TRAP Standard Cell Library Hardware Trojan Risk Assessment and Prevention","Fabless semiconductor manufacturing increases the risk of malicious Hardware Trojan (HT) insertion into integrated circuits, while existing research largely targets HT placement between standard cells. LIB-TRAP introduces a new threat model where standard cells are treated as untrusted, enabling a tampered standard cell library to be used for synthesis and implementation. During fabrication, a malicious foundry swaps deactivated HT cells for activated versions, transforming clean libraries into Trojan-capable ones while masking arbitrary HTs. Open-source and industry EDA flows convert Saed32nm and Sky130nm libraries, evaluate multiple benchmark designs, and train ML classifiers using design-level features to distinguish Trojan-infected from Trojan-free circuits.","LIB-TRAP: Standard Cell Library Hardware Trojan Risk Assessment and Prevention  \nHarish Kumar Dharavath∗ Department of Electrical and Computer Engineering ∗  \nUniversity of Arizona Tucson, Arizona [harrydhara16@arizona.edu](harrydhara16@arizona.edu)  \nMd Muhtasim Alam Chowdhury∗ Department of Electrical and Computer Engineering ∗  \nUniversity of Arizona Tucson, Arizona [mmc7@arizona.edu](mmc7@arizona.edu)  \narXiv :2607 .0 1526v 1 [ cs .CR] 1 Jul 2026  \nRozhin Yasaei† College of Information Sciences†  \nUniversity of Arizona Tucson, Arizona [yasaei@arizona.edu](yasaei@arizona.edu)  \nSoheil Salehi∗ Department of Electrical and Computer Engineering ∗  \nUniversity of Arizona Tucson, Arizona [ssalehi@arizona.edu](ssalehi@arizona.edu)  \nAbstract—Vulnerabilities inherent to the fabless semiconductor manufacturing model have significantly increased the risk of malicious Hardware Trojan (HT) insertion, posing severe threats to hardware security. Several HT mitigation and detection strategies have been developed, and existing works explore the insertion of HTs in the space between standard cells in an integrated circuit. However, there is a lack of research into the vulnerabilities posed by the building blocks of most digital designs on the market today, the standard cells. This work investigates a novel threat model in which standard cells are considered untrusted. Our proposed threat model provides the design house with a tampered standard cell library. The intended netlist is synthesized and implemented using the tampered library. During fabrication, a nefarious foundry replaces the library’s deactivated HT cells with activated counterparts. Using open-source and industry-standard Electronic Design Automation (EDA) tools, existing standard cell libraries, Saed32nm and Sky130nm, are converted into malicious libraries capable of masking the presence of arbitrary HTs from IC designers. The malicious library is then applied and characterized in multiple standard benchmark designs. To demonstrate the efficacy and stealthiness of this standard cell-based attack vector, three benchmark circuits, an AES-128 encryption core, an Ethernet controller, and a WISHBONE DMA engine were synthesized using both clean and Trojan-infected libraries across Synopsys 32nm and SkyWater 130nm technologies. Designlevel features, including total cell count, total area, dynamic power consumption, and static power, were extracted from these synthesized circuits to serve as inputs for binary classification. We trained several conventional Machine Learning (ML) models, including Logistic regression, Random forest, Support Vector Machine (SVM), and Deep Neural Network (DNN), to distinguish between Trojan-infected and Trojan-free designs.  \nIndex Terms—Hardware Trojan, Hardware Security, AES Encryption, VLSI Design, Integrated Circuit, FPGA Design.  \nI. INTRODUCTION  \nA fabless semiconductor manufacturing model is used by many of the major electronics manufacturers. Companies decouple Integrated Circuit (IC) design and fabrication in this  \nmodel and outsource manufacturing to external fabrication Identify applicable funding agency here. If none, delete this.  \nfacilities. These foundries offer cutting-edge facilities and equipment for IC fabrication, Process design kit (PDK), and standard cell libraries. Although external foundries can be cost-effective for IC producers, they are mostly located in separate jurisdictions from the firm designing the IC. This association also introduces numerous security concerns, and given the increasing emphasis on hardware security, it is critical to evaluate and mitigate all foundry-related security risks [1], [2] .  \nHT is a well-researched form of design manipulation in which a nefarious foundry manipulates a design to either extract information or trigger a fault [3] . HTs are often described according to their activation mechanism, known as the trigger, and their effect, referred to as the payload. Detecting HTs often requires a","cbCaiiCTPgFOL7i6","https://ap.wps.com/l/cbCaiiCTPgFOL7i6","pdf",5698821,6,1,7,"English","en",105,"# Introduction\n# Background and Related Work\n# Proposed Threat Model and Contributions","[{\"question\":\"What is the main threat model proposed by LIB-TRAP?\",\"answer\":\"Standard cells are treated as untrusted. A tampered standard cell library is used for synthesis and implementation, and during fabrication a malicious foundry replaces deactivated HT cells with activated counterparts.\"},{\"question\":\"How are malicious standard cell libraries created and validated in this work?\",\"answer\":\"Existing standard cell libraries (Saed32nm and Sky130nm) are converted into malicious libraries using open-source and industry-standard EDA tools, enabling Trojan masking. Benchmark designs are then synthesized and characterized using these libraries across Synopsys 32nm and SkyWater 130nm.\"},{\"question\":\"How does the paper detect or distinguish Trojan-infected designs?\",\"answer\":\"Design-level features—such as total cell count, total area, dynamic power, and static power—are extracted from synthesized circuits and used to train machine learning models (logistic regression, random forest, SVM, and DNN) to classify Trojan-infected versus Trojan-free designs.\"}]","LIB-TRAP Standard Cell Library Hardware Trojan Risk Assessment and Prevention | PDF",1784176399,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"lib-trap-standard-cell-library-hardware-trojan-risk-assessment-and-prevention","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/lib-trap-standard-cell-library-hardware-trojan-risk-assessment-and-prevention/81825/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main threat model proposed by LIB-TRAP?","Question",{"text":77,"@type":78},"Standard cells are treated as untrusted. A tampered standard cell library is used for synthesis and implementation, and during fabrication a malicious foundry replaces deactivated HT cells with activated counterparts.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How are malicious standard cell libraries created and validated in this work?",{"text":82,"@type":78},"Existing standard cell libraries (Saed32nm and Sky130nm) are converted into malicious libraries using open-source and industry-standard EDA tools, enabling Trojan masking. Benchmark designs are then synthesized and characterized using these libraries across Synopsys 32nm and SkyWater 130nm.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the paper detect or distinguish Trojan-infected designs?",{"text":86,"@type":78},"Design-level features—such as total cell count, total area, dynamic power, and static power—are extracted from synthesized circuits and used to train machine learning models (logistic regression, random forest, SVM, and DNN) to classify Trojan-infected versus Trojan-free designs.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]