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◬ AI & Machine Learning May 22, 2026
Safeguarding Text-to-Image Generative Models Against Unauthorized Knowledge Distillation

arXiv:2605.22060v1 Announce Type: new Abstract: Closed-weight generative services are increasingly deployed through query-based APIs, where users can obtain generated outputs while model parameters re…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
RADAR: Defending RAG Dynamically against Retrieval Corruption

arXiv:2605.22041v1 Announce Type: new Abstract: While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing st…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Parser-Free Querying of Security Logs

arXiv:2605.22027v1 Announce Type: new Abstract: Security analysts routinely query system logs to detect threats and investigate incidents, but each log source uses its own semi-structured format: logs…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems

arXiv:2605.22001v1 Announce Type: new Abstract: Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives. We…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers

arXiv:2605.21915v1 Announce Type: new Abstract: Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood. W…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
PEMark: Watermarking API Responses Based on Proxy Gateways and Position Encoding

arXiv:2605.21865v1 Announce Type: new Abstract: Data leakage from API responses has drawn wide attention. APIs are often not fully regulated, making them easy to abuse. One common solution is to embed…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
SPIDER: Two Server Functionality for the Cost of Zero

arXiv:2605.21857v1 Announce Type: new Abstract: We introduce baseSPIDER and SPIDER, private information retrieval (PIR) schemes that embody two technical advancements. The baseSPIDER protocol operates…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Quality-Assured Fuzz Harness Generation via the Four Principles Framework

arXiv:2605.21824v1 Announce Type: new Abstract: Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz …

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox

arXiv:2605.21821v1 Announce Type: new Abstract: Sandbox evasion remains a critical challenge for automated malware analysis, as modern malware employs environment checks to detect analysis platforms a…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Graph Structure of Chebyshev Permutation Polynomials over Binary and Ternary Adic Rings

arXiv:2605.21819v1 Announce Type: new Abstract: Understanding the functional graph of a nonlinear map over a finite domain is crucial for analyzing its dynamical complexity and potential applications …

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Polars inside Intel SGX2 Enclaves: An Empirical Study of Confidential Analytical Query Processing

arXiv:2605.21797v1 Announce Type: new Abstract: Trusted Execution Environments (TEEs) have renewed interest in confidential analytics, but most prior evaluations focus on SQL database engines or earli…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction

arXiv:2605.21779v1 Announce Type: new Abstract: Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise for a…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection

arXiv:2605.21773v1 Announce Type: new Abstract: Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing and …

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
PocketAgents: A Manifest-Driven Library of Autonomous Defense Agents

arXiv:2605.21694v1 Announce Type: new Abstract: Connecting large language models (LLMs) to defensive enforcement requires more than asking a model whether an attack is happening. A defender must decid…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Adversarial Reframing: A Framework for Targeted Generation in Language Models

arXiv:2605.21674v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely deployed in diverse real-world settings, yet remain vulnerable to jailbreaking, where prompt-based attacks bypas…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage

arXiv:2605.21615v1 Announce Type: new Abstract: Existing binary corpora typically capture only one or two axes of binary variation: they either provide cross-compiler builds without a temporal axis, o…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

arXiv:2605.21541v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate en…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Chain Reactions: How Nonce Collisions in ECDSA Compromise Polygon MEV Searchers

arXiv:2605.21498v1 Announce Type: new Abstract: ECDSA signatures form the bedrock of blockchain transaction authentication, yet their security critically depends on proper nonce generation. We uncover…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
Autonomous LLM Agents & CTFs: A Second Look

arXiv:2605.21497v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed to automate offensive security tasks, with recent studies reporting near human-level success…

arXiv Security Read →
◬ AI & Machine Learning May 22, 2026
SK Shieldus researcher paper accepted by ICML 2026 - 디지털투데이

SK Shieldus researcher paper accepted by ICML 2026 디지털투데이

디지털투데이 Read →
◬ AI & Machine Learning May 22, 2026
Roundtables: Can AI Learn to Understand the World?

Listen to the session or watch below AI companies want to build systems that understand the external world and overcome the limitations of LLMs. Recent developments have brought world models to the fo…

MIT Tech Review AI Read →
◬ AI & Machine Learning May 21, 2026
Scaling creativity in the age of AI

Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from ea…

MIT Tech Review AI Read →
◬ AI & Machine Learning May 21, 2026
State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents - Darktrace

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents Darktrace

Darktrace Read →
◬ AI & Machine Learning May 21, 2026
Anthropic’s Code with Claude showed off coding’s future—whether you like it or not

The vibes were strong at Code with Claude, Anthropic’s two-day event for software developers in London that kicked off on May 19, the same day as Google’s I/O in Palo Alto. (A coincidence, not a flex,…

MIT Tech Review AI Read →
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