Misconfigured Server Reveals Three Evilginx Phishing Operations Targeting Microsoft 365
An attacker running a live Microsoft 365 phishing operation left a Python web server listening on a public port with directory listing switched on. The command that did it: python3 -m http.server 8080, was still sitting in the readable .bash_history. From that one lapse, French security firm Lexfo lifted the operator's entire toolkit and pivoted through it to two more
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Leveraging Interpretable Tsetlin Machine for PDF Malware Detectionarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Leveraging Interpretable Tsetlin Machine for PDF Malware Detection
arXiv:2607.09290v1 Announce Type: new Abstract: In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same capabilities have also made PDF files an attractive attack vector for cyberattackers, who embed malicious code within seemingly legitimate document…
Read original ↗https://arxiv.org/abs/2607.09290 SFDS: Selective File Disclosure Systemarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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SFDS: Selective File Disclosure System
arXiv:2607.09370v1 Announce Type: new Abstract: Access control to networked resources has been a longstanding challenge. The conventional solution relies on authentication mechanisms, which introduce additional complexities associated with Identity and Access Management (IAM). Such systems require user authentication, identity management, and authorization services, while also introducing security risks arising from vulnerabilities, mis…
Read original ↗https://arxiv.org/abs/2607.09370 Ruby: Unmasking Unsafe Rust in Stripped Binaries via Machine Learningarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Ruby: Unmasking Unsafe Rust in Stripped Binaries via Machine Learning
arXiv:2211.00111v3 Announce Type: replace Abstract: Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation. As a result, memory safety bugs are typically confined to the $\texttt{unsafe}$ regions, which have been the primary focus of Rust bug-finding tools. However, such tools rely on the presence of the $\texttt{unsafe}…
Read original ↗https://arxiv.org/abs/2211.00111 Malaika: Understanding Malware through Tri-Grounded Agentic Reasoningarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
arXiv:2607.09179v1 Announce Type: new Abstract: Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct high-level malicious behaviors under partial observability from sparse, dispersed evidence intertwined with benign functionality. While static analysis can expose security-relevant…
Read original ↗https://arxiv.org/abs/2607.09179 Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requestsarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests
arXiv:2607.09259v1 Announce Type: new Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests, and its main result is that the…
Read original ↗https://arxiv.org/abs/2607.09259 Efficient and Universal Watermarking for LLM-Generated Code Detectionarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Efficient and Universal Watermarking for LLM-Generated Code Detection
arXiv:2402.07518v5 Announce Type: replace Abstract: Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Waterm…
Read original ↗https://arxiv.org/abs/2402.07518 Portable Acceleration of Learning With Errors KEMs for Post-Quantum Cryptographyarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Portable Acceleration of Learning With Errors KEMs for Post-Quantum Cryptography
arXiv:2607.09541v1 Announce Type: new Abstract: The transition to post-quantum cryptography (PQC) is driving demand for implementations that can meet the computational requirements of real-world applications. Among the proposed PQC constructions, Learning With Errors (LWE) based key encapsulation mechanisms (KEMs) are particularly attractive due to their strong security foundations, but they inc…
Read original ↗https://arxiv.org/abs/2607.09541 SLBench: Evaluating How LLM Agents Follow Logical Relations in Skillsarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills
arXiv:2607.09016v1 Announce Type: new Abstract: Agent skills extend LLM agents with reusable procedures, tools, and domain-specific workflows, but their safety depends on resolving dependencies among interacting instructions. We introduce SkillLogic, a framework for analyzing logical relations in skill files and constructing executable tests from them. Our taxonomy covers eight relation types, including pr…
Read original ↗https://arxiv.org/abs/2607.09016 RaMark: Radioactive Watermarking for Generated Tabular Dataarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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RaMark: Radioactive Watermarking for Generated Tabular Data
arXiv:2607.09000v1 Announce Type: new Abstract: Recent advances in generative modeling have made generated tabular data a practical solution for privacy-sensitive data sharing, where watermarking enables ownership verification. However, existing watermarking methods fundamentally fail under retraining attacks, in which an adversary retrains a generative model on a watermarked dataset and regenerates high-utility dat…
Read original ↗https://arxiv.org/abs/2607.09000 Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documentsarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documents
arXiv:2607.09011v1 Announce Type: new Abstract: Exposed documents such as emails, chat threads, tickets, and incident notes routinely leak credentials, but during incident response a leaked secret is only half the story. Responders also need to identify the ``door'' the secret opens: the account, tenant, endpoint, database, cloud resource, or other system that the credential could allow a…
Read original ↗https://arxiv.org/abs/2607.09011 SeedSmith: LLM-Driven Seed Synthesis for Directed Fuzzingarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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SeedSmith: LLM-Driven Seed Synthesis for Directed Fuzzing
arXiv:2607.08949v1 Announce Type: new Abstract: Directed fuzzing steers fuzzers toward user-defined sink functions to identify vulnerabilities, but it frequently fails to trigger crashes even after long campaigns. We identify two challenges that prevent directed fuzzers from exposing crashes: incomplete static analysis of indirect calls, which leaves reachable paths invisible to distance-based guidance, and lack of se…
Read original ↗https://arxiv.org/abs/2607.08949 Federated Learning Architecture: Data Privacy and System Security Approachesarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Federated Learning Architecture: Data Privacy and System Security Approaches
arXiv:2607.09391v1 Announce Type: new Abstract: This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning (FL) systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information may still be leaked during model updates. To address th…
Read original ↗https://arxiv.org/abs/2607.09391 VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agentsarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
arXiv:2607.09653v1 Announce Type: new Abstract: Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments,…
Read original ↗https://arxiv.org/abs/2607.09653 Entropy Bootstrapping for Wireless Embedded Systemsarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Entropy Bootstrapping for Wireless Embedded Systems
arXiv:2607.08865v1 Announce Type: new Abstract: Weak randomness has broken deployed cryptography through implementation bugs, boot entropy scarcity, and backdoored generators. Inexpensive wireless sensors concentrate the risk because many boot or operate in highly deterministic conditions while relying on basic, rudimentary, or opaque RNGs. On ESP32-class boards, RF-disabled wireless device RNG register (WDEV) output is pse…
Read original ↗https://arxiv.org/abs/2607.08865 Privacy-Preserving Intent Fulfilment and Assurance for 6G RANarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Privacy-Preserving Intent Fulfilment and Assurance for 6G RAN
arXiv:2607.08809v1 Announce Type: new Abstract: Intent-based network management is the emerging paradigm for 6G service lifecycle automation, with the 3GPP intent management framework (TS~28.312) defining creation, translation, fulfilment, and assurance stages. Existing fulfilment and assurance approaches require deep packet inspection, per-flow state tracking, or access to vendor-internal node telemetry to verify…
Read original ↗https://arxiv.org/abs/2607.08809 Statistically Undetectable Backdoors in Deep Neural Networksarxiv_cs_cr · tlp:amber · 7/13/2026, 4:00:00 AM
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Statistically Undetectable Backdoors in Deep Neural Networks
arXiv:2607.09532v1 Announce Type: cross Abstract: We show how an adversarial model trainer can plant backdoors in a large class of deep, feedforward neural networks. These backdoors are statistically undetectable in the white-box setting, meaning that the backdoored and honestly trained models are close in total variation distance, even given the full descriptions of the models (e.g., all of the weights). The backd…
Read original ↗https://arxiv.org/abs/2607.09532 Kernel prepatch 7.2-rc3lwn_kernel · tlp:amber · 7/12/2026, 11:29:47 PM
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Kernel prepatch 7.2-rc3
The 7.2-rc3 kernel prepatch is out for testing. Linus said: " Things continue to look normal (the 'new normal' with slightly higher rates of commits, although I do get the feeling that we're seeing that slightly balanced out by people starting to go on summer vacation) ".
Kernel prepatch 7.2-rc3 [LWN.net] LWN .net News from the source Content Weekly Edition Archives Search Kernel Security Events calendar Unread comments LWN FAQ Write for us User: Pas…
Read original ↗https://lwn.net/Articles/1082487 
Misconfigured Server Reveals Three Evilginx Phishing Operations Targeting Microsoft 365
