MalEval Android Malware Evaluation Framework

This repository contains the source code of MalEval, an evaluation framework for Android malware behavior auditing, focusing on explaining and substantiating malicious behaviors. The framework provides expert-verified reports, curated metadata, and model outputs to enable reproducible evaluation ...

Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing

Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challengin...

Cyber Threat Hunting: Non-Parametric Mining of Attack Patterns from Cyber Threat Intelligence for Precise Threats Attribution

With the ever-changing landscape of cyber threats, identifying their origin has become paramount, surpassing the simple task of attack classification. Cyber threat attribution gives security analysts the insights they need to device effective threat mitigation strategies. Such strategies empower...

Brute Force

Overview Affected versions of this package are vulnerable to Brute Force via the authentication process in the Userpass or LDAP systems. An attacker can circumvent intended user lockout protections by exploiting differences in user entity alias attribution between pre-flight and full login...

AuthPrint: Fingerprinting Generative Models against Malicious Model Providers

Generative models are increasingly adopted in high-stakes domains, yet current deployments offer no mechanisms to verify the origin of model outputs. We address this gap by extending model fingerprinting techniques beyond the traditional collaborative setting to one where the model provider may a...

FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...

Mechanistic Interpretability in the Presence of Architectural Obfuscation

Architectural obfuscation - e.g., permuting hidden-state tensors, linearly transforming embedding tables, or remapping tokens - has recently gained traction as a lightweight substitute for heavyweight cryptography in privacy-preserving large-language-model LLM inference. While recent work has sho...

LLM Embedding-Based Attribution (LEA): Quantifying Source Contributions to Generative Model'S Response for Vulnerability Analysis

Security vulnerabilities are rapidly increasing in frequency and complexity, creating a shifting threat landscape that challenges cybersecurity defenses. Large Language Models LLMs have been widely adopted for cybersecurity threat analysis. When querying LLMs, dealing with new, unseen...

AURA: a Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution

Effective attribution of Advanced Persistent Threats APTs increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA Attribution Using Retrieval-Augmented Agents, a multi-agent, knowledge-enhanced framework fo...

TracLLM: a Generic Framework for Attributing Long Context LLMs

Long context large language models LLMs are deployed in many real-world applications such as RAG, agent, and broad LLM-integrated applications. Given an instruction and a long context e.g., documents, PDF files, webpages, a long context LLM can generate an output grounded in the provided context,...

Microsoft and CrowdStrike Launch Shared Threat Actor Glossary to Cut Attribution Confusion

Microsoft and CrowdStrike have announced that they are teaming up to align their individual threat actor taxonomies by publishing a new joint threat actor mapping. "By mapping where our knowledge of these actors align, we will provide security professionals with the ability to connect insights...

Watermarking without Standards Is Not AI Governance

Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a...

Zero-Trust Foundation Models: a New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models ZTFMs, a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models FMs for Internet of Things IoT systems. By integrating core tenets, such as continuous verification, least privilege access LPA, data...

CVE-2023-21096

In OnWakelockReleased of attributionprocessor.cc, there is a use after free that could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12 Android-12L Android-13Android ID: A-254774758...

CVE-2023-20934

In resolveAttributionSource of ServiceUtilities.cpp, there is a possible way to disable the microphone privacy indicator due to a permissions bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for...

CVE-2020-27621

The FileImporter extension in MediaWiki through 1.35.0 was not properly attributing various user actions to a specific user's IP address. Instead, for various actions, it would report the IP address of an internal Wikimedia Foundation server by omitting X-Forwarded-For data. This resulted in an...

On Technique Identification and Threat-Actor Attribution Using LLMs and Embedding Models

Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation causes significant attribution delays, especially following major incidents at the international scale. This research...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks

Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the applicability of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker's true network...

CachePrune: Neural-Based Attribution Defense against Indirect Prompt Injection Attacks

Large Language Models LLMs are identified as being susceptible to indirect prompt injection attack, where the model undesirably deviates from user-provided instructions by executing tasks injected in the prompt context. This vulnerability stems from LLMs' inability to distinguish between data and...