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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
ColumnDisturb: Understanding Column-based Read Disturbanc...
İsmail Emir Yüksel, Ataberk Olgun, F. Nisa Bostancı, Haocong Luo · 2025-10-16 · via cs.CR updates on arXiv.org

We experimentally demonstrate a new widespread read disturbance phenomenon, ColumnDisturb, in real commodity DRAM chips. By repeatedly opening or keeping a DRAM row (aggressor row) open, we show that it is possible to disturb DRAM cells through a DRAM column (i.e., bitline) and induce bitflips in DRAM cells sharing the same columns as the aggressor row (across multiple DRAM subarrays). With ColumnDisturb, the activation of a single row concurrently disturbs cells across as many as three subarrays (e.g., 3072 rows) as opposed to RowHammer/RowPress, which affect only a few neighboring rows of the aggressor row in a single subarray. We rigorously characterize ColumnDisturb and its characteristics under various operational conditions using 216 DDR4 and 4 HBM2 chips from three major manufacturers. Among our 27 key experimental observations, we highlight two major results and their implications. First, ColumnDisturb affects chips from all three major manufacturers and worsens as DRAM technology scales down to smaller node sizes (e.g., the minimum time to induce the first ColumnDisturb bitflip reduces by up to 5.06x). We observe that, in existing DRAM chips, ColumnDisturb induces bitflips within a standard DDR4 refresh window (e.g., in 63.6 ms) in multiple cells. We predict that, as DRAM technology node size reduces, ColumnDisturb would worsen in future DRAM chips, likely causing many more bitflips in the standard refresh window. Second, ColumnDisturb induces bitflips in many (up to 198x) more rows than retention failures. Therefore, ColumnDisturb has strong implications for retention-aware refresh mechanisms that leverage the heterogeneity in cell retention times: our detailed analyses show that ColumnDisturb greatly reduces the benefits of such mechanisms.