A Measurement Study of Cryptographic Misuse in Embodied AI Mobile Applications

• URL: http://arxiv.org/abs/2606.19983v1
• Date: Thu, 18 Jun 2026 09:24:22 GMT
• Title: A Measurement Study of Cryptographic Misuse in Embodied AI Mobile Applications
• Abstract summary: Embodied AI (EAI) mobile applications are evolving from auxiliary user interfaces into active control-path components.<n>We present the first large-scale measurement study of cryptographic misuse within the EAI mobile ecosystem.
• Score: 18.86199323484397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied AI (EAI) mobile applications are evolving from auxiliary user interfaces into active control-path components, directly linking mobile-side cryptographic security to cyber-physical trust. Despite this shift, existing security research predominantly focuses on embodied AI devices and cloud infrastructures, leaving the mobile control layer largely unexplored as a critical attack surface. To bridge this gap, we present the first large-scale measurement study of cryptographic misuse within the EAI mobile ecosystem. We construct EAIAppZoo, a benchmark of 507 real-world applications across six EAI domains, and employ an automated semantic-aware analysis pipeline to measure the prevalence and characteristics of five major cryptographic failure modes. Our measurement yields 12,975 misuse findings (with an evaluated precision of 80.74\%), revealing that these cryptographic failures are driven by EAI-specific engineering constraints rather than random developer errors. We uncover structural security trade-offs: latency-sensitive control paths systematically weaken transport protection, while the heavy reliance on offline device provisioning and legacy IoT SDKs exacerbates the local hardcoding of authentication credentials. Through real-world case studies, we demonstrate how these mobile-side cryptographic flaws bypass nominal network protections, enabling adversaries to intercept command channels and hijack the physical control of EAI entities. Ultimately, our findings highlight that mobile applications have become a fragile, yet overlooked, cryptographic trust boundary in cyber-physical systems.

Related papers

• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• Understanding AI Methods for Intrusion Detection and Cryptographic Leakage [42.8384136157456]
  We evaluate how machine learning techniques can detect malicious network activity and identify potential information leakage in cryptographic implementations.<n>Our results demonstrate that AI models can achieve near-perfect detection accuracy within stable network environment.<n>We also observed that learned models identify patterns consistent with side-channel leakage, suggesting that AI can assist in uncovering implementation-level vulnerabilities.
  arXiv  Detail & Related papers  (2026-03-26T18:42:57Z)
• Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw [42.9467373594646]
  This report presents a comprehensive security analysis of the OpenClaw ecosystem.<n>We highlight critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination.<n>To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA)<n>This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation.
  arXiv  Detail & Related papers  (2026-03-13T04:33:05Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space [42.234025453061875]
  Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics.<n>We introduce the State Backdoor, a novel and practical backdoor attack that leverages the robot arm's initial state as the trigger.<n>Our method achieves over 90% attack success rate without affecting benign task performance, revealing an underexplored vulnerability in embodied AI systems.
  arXiv  Detail & Related papers  (2026-01-07T08:54:31Z)
• MAIF: Enforcing AI Trust and Provenance with an Artifact-Centric Agentic Paradigm [0.5495755145898128]
  Current AI systems operate on opaque data structures that lack the audit trails, provenance tracking, or explainability required by emerging regulations like the EU AI Act.<n>We propose an artifact-centric AI agent paradigm where behavior is driven by persistent, verifiable data artifacts rather than ephemeral tasks.<n>Production-ready implementation demonstrates ultra-high-speed streaming (2,720.7 MB/s), optimized video processing (1,342 MB/s), and enterprise-grade security.
  arXiv  Detail & Related papers  (2025-11-19T04:10:32Z)
• Cybersecurity AI: Humanoid Robots as Attack Vectors [0.448741371377488]
  We present a systematic security assessment of the Unitree G1 humanoid.<n>We show it operates simultaneously as a covert surveillance node and can be purposed as an active cyber operations platform.
  arXiv  Detail & Related papers  (2025-09-17T16:18:53Z)
• ANNIE: Be Careful of Your Robots [48.89876809734855]
  We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
  arXiv  Detail & Related papers  (2025-09-03T15:00:28Z)
• CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
  This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
  arXiv  Detail & Related papers  (2025-07-19T20:09:52Z)
• AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles [1.1797787239802762]
  We develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity.<n>This framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity.<n>This makes the AV system more resilient to attacks from both cyberspace and hardware component failure.
  arXiv  Detail & Related papers  (2025-05-10T12:53:28Z)
• Physical and Software Based Fault Injection Attacks Against TEEs in Mobile Devices: A Systemisation of Knowledge [5.6064476854380825]
  Trusted Execution Environments (TEEs) are critical components of modern secure computing. They provide isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks.
  arXiv  Detail & Related papers  (2024-11-22T11:59:44Z)
• LuaTaint: A Static Analysis System for Web Configuration Interface Vulnerability of Internet of Things Devices [27.9668893670548]
  This study introduces an automated vulnerability detection system, called LuaTaint, for the commonly used web configuration interface of IoT devices. LuaTaint combines static taint analysis with a large language model (LLM) to achieve widespread and high-precision detection. We develop a prototype of LuaTaint and evaluate it using 2,447 IoT firmware samples from 11 renowned vendors.
  arXiv  Detail & Related papers  (2024-02-25T09:52:02Z)
• Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection [0.0]
  In cybersecurity, the sensitive data along with the contextual information and high-quality labeling play an essential role. In this paper, we investigate a novel robust aggregation method for federated learning, namely Fed-LSAE, which takes advantage of latent space representation. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks.
  arXiv  Detail & Related papers  (2023-09-20T04:14:48Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.