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Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Turning Hearsay into Discovery: Industrial 3D Printer Sid...
Aleksandr Dolgavin, Jacob Gatlin, Moti Yung, Mark Yampolskiy · 2025-09-23 · via cs.CR updates on arXiv.org

The central security issue of outsourced 3D printing (aka AM: Additive Manufacturing), an industry that is expected to dominate manufacturing, is the protection of the digital design (containing the designers' model, which is their intellectual property) shared with the manufacturer. Here, we show, for the first time, that side-channel attacks are, in fact, a concrete serious threat to existing industrial grade 3D printers, enabling the reconstruction of the model printed (regardless of employing ways to directly conceal the design, e.g. by encrypting it in transit and before loading it into the printer). Previously, such attacks were demonstrated only on fairly simple FDM desktop 3D printers, which play a negligible role in manufacturing of valuable designs. We focus on the Powder Bed Fusion (PBF) AM process, which is popular for manufacturing net-shaped parts with both polymers and metals. We demonstrate how its individual actuators can be instrumented for the collection of power side-channel information during the printing process. We then present our approach to reconstruct the 3D printed model solely from the collected power side-channel data. Further, inspired by Differential Power Analysis, we developed a method to improve the quality of the reconstruction based on multiple traces. We tested our approach on two design models with different degrees of complexity. For different models, we achieved as high as 90.29~\% of True Positives and as low as 7.02~\% and 9.71~\% of False Positives and False Negatives by voxel-based volumetric comparison between reconstructed and original designs. The lesson learned from our attack is that the security of design files cannot solely rely on protecting the files themselves in an industrial environment, but must instead also rely on assuring no leakage of power, noise and similar signals to potential eavesdroppers in the printer's vicinity.