TrustConnect: an In-Vehicle Anomaly Detection Framework through Topology-Based Trust Rating

Modern vehicles are equipped with numerous in-vehicle components that interact with the external environment through remote communications and services, such as Bluetooth and vehicle-to-infrastructure communication. These components form a network, exchanging information to ensure the proper...

Wireshark Analyzer 4.4.7

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers. Thi...

Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance

Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning...

Obfuscation-Resilient Binary Code Similarity Analysis Using Dominance Enhanced Semantic Graph

Binary code similarity analysis BCSA serves as a core technique for binary analysis tasks such as vulnerability detection. While current graph-based BCSA approaches capture substantial semantics and show strong performance, their performance suffers under code obfuscation due to the unstable...

XWiki 15.10.10 Remote Code Execution

XWiki versions up to 15.10.10 proof of concept remote code execution exploit written in go...

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems

Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats APTs, by exposing complex attack patterns. While recent systems combine graph neural networks GNNs with natural language processing NLP to capture structural and semantic features...

PoCGen: Generating Proof-Of-Concept Exploits for Vulnerabilities in Npm Packages

Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring the integrity and security of software systems. However, previous work has shown that vulnerability reports often lack...

Scoring the Unscorables: Cyber Risk Assessment beyond Internet Scans

In this paper we present a study on using novel data types to perform cyber risk quantification by estimating the likelihood of a data breach. We demonstrate that it is feasible to build a highly accurate cyber risk assessment model using public and readily available technology signatures obtaine...

QualitEye: Public and Privacy-Preserving Gaze Data Quality Verification

Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye--the...

A Certified Unlearning Approach without Access to Source Data

With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the...

Synthetic Tabular Data: Methods, Attacks and Defenses

Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning an...

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-Level Unlearning Completeness

Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining. Techniques like Membership Inference Attacks MIAs are widely used to externally assess successful unlearning. However,...

PrivTru: a Privacy-By-Design Data Trustee Minimizing Information Leakage

Data trustees serve as intermediaries that facilitate secure data sharing between independent parties. This paper offers a technical perspective on Data trustees, guided by privacy-by-design principles. We introduce PrivTru, an instantiation of a data trustee that provably achieves optimal privac...

Stealix: Model Stealing Via Prompt Evolution

Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data...

Falco 0.41.1

Sysdig Falco is a behavioral activity monitoring agent that is open source and comes with native support for containers. Falco lets you define highly granular rules to check for activities involving file and network activity, process execution, IPC, and much more, using a flexible syntax. Falco...

SATversary: Adversarial Attacks on Satellite Fingerprinting

As satellite systems become increasingly vulnerable to physical layer attacks via SDRs, novel countermeasures are being developed to protect critical systems, particularly those lacking cryptographic protection, or those which cannot be upgraded to support modern cryptography. Among these is...

WordPress HyperComments 1.2.2 Privilege Escalation

WordPress HyperComments plugin versions 1.2.2 and below suffer from an unauthenticated remote privilege escalation vulnerability...

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Large language model LLM watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated i.e., watermarked predictions depend on random side...

FIST: a Structured Threat Modeling Framework for Fraud Incidents

Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework Fraud Incident Structured Threat Framework, an innovative structured threat modeling...

Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models

The protection of cyber Intellectual Property IP such as web content is an increasingly critical concern. The rise of large language models LLMs with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users...

Conformal-DP: Data Density Aware Privacy on Riemannian Manifolds Via Conformal Transformation

Whitepaper called Conformal-DP: Data Density Aware Privacy On Riemannian Manifolds Via Conformal Transformation...

What Really Is a Member? Discrediting Membership Inference Via Poisoning

Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often fail under the strict definition of membership based on exact matching, and have suggested relaxing this definition t...

A Systematic Review of Poisoning Attacks against Large Language Models

With the widespread availability of pretrained Large Language Models LLMs and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part...

GNUnet P2P Framework 0.24.2

GNUnet is a peer-to-peer framework with focus on providing security. All peer-to-peer messages in the network are confidential and authenticated. The framework provides a transport abstraction layer and can currently encapsulate the network traffic in UDP IPv4 and IPv6, TCP IPv4 and IPv6, HTTP, o...

Detecting and Mitigating SQL Injection Vulnerabilities in Web Applications

SQL injection SQLi remains a critical vulnerability in web applications, enabling attackers to manipulate databases through malicious inputs. Despite advancements in mitigation techniques, the evolving complexity of web applications and attack strategies continues to pose significant risks. This...

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models

Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial...

There'S Waldo: PCB Tamper Forensic Analysis Using Explainable AI on Impedance Signatures

The security of printed circuit boards PCBs has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. While detecting tampering on a PCB is the first step for verification, forensics is also needed to identify the modified...

Membership Inference Attacks for Unseen Classes

Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background nonmember data distribution that matches the distribution the target model was trained on. We initiat...

When Better Features Mean Greater Risks: the Performance-Privacy Trade-Off in Contrastive Learning

With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk o...

Depermissioning Web3: a Permissionless Accountable RPC Protocol for Blockchain Networks

In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain...

Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach

Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper develops the first methods that accurately...

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Differentially private stochastic gradient descent DP-SGD is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in DP-SGD is setting the per-sample gradient clipping threshold, which significantly affects the trade-off between privac...

Rethinking Machine Unlearning in Image Generation Models

With the surge and widespread application of image generation models, data privacy and content safety have become major concerns and attracted great attention from users, service providers, and policymakers. Machine unlearning MU is recognized as a cost-effective and promising means to address...

Adapting under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security

Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems NIDS. The rise of these changing patterns has exposed the limitations of traditional network security methods. While signature-based methods are used to detect different types of attacks, th...

The Scales of Justitia: a Comprehensive Survey on Safety Evaluation of LLMs

With the rapid advancement of artificial intelligence technology, Large Language Models LLMs have demonstrated remarkable potential in the field of Natural Language Processing NLP, including areas such as content generation, human-computer interaction, machine translation, and code generation,...

Faraday 5.14.1

Faraday is a tool that introduces a new concept called IPE, or Integrated Penetration-Test Environment. It is a multiuser penetration test IDE designed for distribution, indexation and analysis of the generated data during the process of a security audit. The main purpose of Faraday is to re-use...

To Protect the LLM Agent against the Prompt Injection Attack with Polymorphic Prompt

LLM agents are widely used as agents for customer support, content generation, and code assistance. However, they are vulnerable to prompt injection attacks, where adversarial inputs manipulate the model's behavior. Traditional defenses like input sanitization, guard models, and guardrails are...

Combating Reentrancy Bugs on Sharded Blockchains

Reentrancy is a well-known source of smart contract bugs on Ethereum, leading e.g. to double-spending vulnerabilities in DeFi applications. But less is known about this problem in other blockchains, which can have significantly different execution models. Sharded blockchains in particular general...

Breaking the Gaussian Barrier: Residual-PAC Privacy for Automatic Privatization

The Probably Approximately Correct PAC Privacy framework 1 provides a powerful instance-based methodology for certifying privacy in complex data-driven systems. However, existing PAC Privacy algorithms rely on a Gaussian mutual information upper bound. We show that this is in general too...

The Complexity of the SupportMinors Modeling for the MinRank Problem

In this note, we provide proven estimates for the complexity of the SupportMinors Modeling, mostly confirming the heuristic complexity estimates contained in the original article...

Securing Traffic Sign Recognition Systems in Autonomous Vehicles

Deep Neural Networks DNNs are widely used for traffic sign recognition because they can automatically extract high-level features from images. These DNNs are trained on large-scale datasets obtained from unknown sources. Therefore, it is important to ensure that the models remain secure and are n...

NIH BRICS 14.0.0-67 Predictable Tokens

NIH BRICS aka Biomedical Research Informatics Computing System through 14.0.0-67 generates predictable tokens that depend on username, time, and the fixed 7Dl9dj- string and thus allows unauthenticated users with a Common Access Card CAC to escalate privileges and compromise any account, includin...

Benchmarking Misuse Mitigation against Covert Adversaries

Existing language model safety evaluations focus on overt attacks and low-stakes tasks. Realistic attackers can subvert current safeguards by requesting help on small, benign-seeming tasks across many independent queries. Because individual queries do not appear harmful, the attack is hard to...

On Automating Security Policies with Contemporary LLMs

The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models LLMs for automating attack mitigation policy...

EMBER2024 -- a Benchmark Dataset for Holistic Evaluation of Malware Classifiers

A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow scope - most include files targeting a single platform, have labels supporting...

Authenticated Private Set Intersection: a Merkle Tree-Based Approach for Enhancing Data Integrity

Private Set Intersection PSI enables secure computation of set intersections while preserving participant privacy, standard PSI existing protocols remain vulnerable to data integrity attacks allowing malicious participants to extract additional intersection information or mislead other parties. I...

Why LLM Safety Guardrails Collapse after Fine-Tuning: a Similarity Analysis between Alignment and Fine-Tuning Datasets

Recent advancements in large language models LLMs have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrail...

Hybrid Stabilization Protocol for Cross-Chain Digital Assets Using Adaptor Signatures and AI-Driven Arbitrage

Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance. We present a hybrid stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain liquidity pools to achieve robust price adherence...

Network Hexagons under Attack: Secure Crowdsourcing of Geo-Referenced Data

A critical requirement for modern-day Intelligent Transportation Systems ITS is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol LISP and the...

Inclusive, Differentially Private Federated Learning for Clinical Data

Federated Learning FL offers a promising approach for training clinical AI models without centralizing sensitive patient data. However, its real-world adoption is hindered by challenges related to privacy, resource constraints, and compliance. Existing Differential Privacy DP approaches often app...