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cs.CR updates on arXiv.org

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
Pentest on an Internet Mobile App: A Case Study using Tra...
Daniel Dalalana Bertoglio, Guilherme Girotto, Charles Varlei Neu · 2019-12-20 · via cs.CR updates on arXiv.org

Mobile applications are used to handle different types of data. Commonly, there is a set of personal identifiable information present in the data stored, shared and used by these applications. From that, attackers can try to exploit the mobile application in order to obtain or to cause private data leakage. Therefore, performing security assessments is an important practice to find vulnerabilities in the applications and systems before the application is deployed, or even during their use. Regarding security assessments, Penetration Test (Pentest) is one of the security test types that can be used to detect vulnerabilities through simulated attacks. Additionally, Pentest can be performed using different methodologies and best practices, through several frameworks to: organize the test execution, execute tools, provide estimations, provide reports and document a Pentest. One such framework is Tramonto, which aims to assist a cybersecurity expert during the Pentest execution by providing organization, standardization and flexibility to the whole Pentest process. This paper presents a Pentest case study applied to a Brazilian university Mobile App using the Tramonto framework. The main goal of this case study is to present how Tramonto can be applied during a Pentest execution, assisting cybersecurity experts in the tasks included in the Pentest process. Our results show details on how to perform a Pentest using Tramonto and the found vulnerabilities in the Mobile App. Besides that, there is a discussion about the main contributions obtained from our results, and we were able to verify that Tramonto managed, organized and optimized the whole Pentest process.