Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention

Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under...

Digital Forensic Investigation of the ChatGPT Windows Application

The ChatGPT Windows application offers better user interaction in the Windows operating system OS by enhancing productivity and streamlining the workflow of ChatGPT's utilization. However, there are potential misuses associated with this application that require rigorous forensic analysis. This...

A Tertiary Review on Quantum Cryptography

Quantum computers impose an immense threat to system security. As a countermeasure, new cryptographic classes have been created to prevent these attacks. Technologies such as post-quantum cryptography and quantum cryptography. Quantum cryptography uses the principle of quantum physics to produce...

Bayesian Perspective on Memorization and Reconstruction

We introduce a new Bayesian perspective on the concept of data reconstruction, and leverage this viewpoint to propose a new security definition that, in certain settings, provably prevents reconstruction attacks. We use our paradigm to shed new light on one of the most notorious attacks in the...

Hijacking Large Language Models Via Adversarial In-Context Learning

In-context learning ICL has emerged as a powerful paradigm leveraging LLMs for specific downstream tasks by utilizing labeled examples as demonstrations demos in the preconditioned prompts. Despite its promising performance, crafted adversarial attacks pose a notable threat to the robustness of...

Falco 0.41.0

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...

Practical Bayes-Optimal Membership Inference Attacks

We develop practical and theoretically grounded membership inference attacks MIAs against both independent and identically distributed i.i.d. data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership...

MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits Using Improved Preference Alignment

The model context protocol MCP has been widely adapted as an open standard enabling the seamless integration of generative AI agents. However, recent work has shown the MCP is susceptible to retrieval-based "falsely benign" attacks FBAs, allowing malicious system access and credential theft, but...

An Advanced Cyber-Physical System Security Testbed for Substation Automation

A Cyber-Physical System CPS testbed serves as a powerful platform for testing and validating cyber intrusion detection and mitigation strategies in substations. This study presents the design and development of a CPS testbed that can effectively assess the real-time dynamics of a substation. Cybe...

Joint Data Hiding and Partial Encryption of Compressive Sensed Streams

The paper proposes a method to secure the Compressive Sensing CS streams. It consists in protecting part of the measurements by a secret key and inserting the code into the rest. The secret key is generated via a cryptographically secure pseudo-random number generator CSPRNG and XORed with the...

The End of Universal Lifelong Identifiers: Identity Systems for the AI Era

Many identity systems assign a single, static identifier to an individual for life, reused across domains like healthcare, finance, and education. These Universal Lifelong Identifiers ULIs underpin critical workflows but now pose systemic privacy risks. We take the position that ULIs are...

HoneySat: a Network-Based Satellite Honeypot Framework

Satellites are the backbone of several mission-critical services, such as GPS that enable our modern society to function. For many years, satellites were assumed to be secure because of their indecipherable architectures and the reliance on security by obscurity. However, technological advancemen...

Synopsis: Secure and Private Trend Inference from Encrypted Semantic Embeddings

WhatsApp and many other commonly used communication platforms guarantee end-to-end encryption E2EE, which requires that service providers lack the cryptographic keys to read communications on their own platforms. WhatsApp's privacy-preserving design makes it difficult to study important phenomena...

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model

The turnstile continual release model of differential privacy captures scenarios where a privacy-preserving real-time analysis is sought for a dataset evolving through additions and deletions. In typical applications of real-time data analysis, both the length of the stream $T$ and the size of th...

SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?

Fine-tuning large language models LLMs for telecom tasks and datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to how this process may compromise model safety. Recent research has shown that even benign fine-tuning can...

Demonstration of Quantum-Secure Communications in a Nuclear Reactor

Quantum key distribution QKD, one of the latest cryptographic techniques, founded on the laws of quantum mechanics rather than mathematical complexity, promises for the first time unconditional secure remote communications. Integrating this technology into the next generation nuclear systems -...

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control

Traditional Identity and Access Management IAM systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect OIDC, and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at...

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

We introduce the Robust Audio Watermarking Benchmark RAW-Bench, a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline with various distortions such as...

Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space

Large Language Models LLMs, despite advanced general capabilities, still suffer from numerous safety risks, especially jailbreak attacks that bypass safety protocols. Understanding these vulnerabilities through black-box jailbreak attacks, which better reflect real-world scenarios, offers critica...

Does Johnny Get the Message? Evaluating Cybersecurity Notifications for Everyday Users

Due to the increasing presence of networked devices in everyday life, not only cybersecurity specialists but also end users benefit from security applications such as firewalls, vulnerability scanners, and intrusion detection systems. Recent approaches use large language models LLMs to rewrite...

Test-Time Immunization: a Universal Defense Framework against Jailbreaks for (Multimodal) Large Language Models

While multimodal large language models LLMs have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defense methods are proposed to defend against jailbreak attacks, however, they are often tailored to specific types of...

Domainator: Detecting and Identifying DNS-Tunneling Malware Using Metadata Sequences

In recent years, malware with tunneling or: covert channel capabilities is on the rise. While malware research led to several methods and innovations, the detection and differentiation of malware solely based on its DNS tunneling features is still in its infancy. Moreover, no work so far has used...

Accountable, Scalable and DoS-Resilient Secure Vehicular Communication

Paramount to vehicle safety, broadcasted Cooperative Awareness Messages CAMs and Decentralized Environmental Notification Messages DENMs are pseudonymously authenticated for security and privacy protection, with each node needing to have all incoming messages validated within an expiration...

A Comparative Study of Fuzzers and Static Analysis Tools for Finding Memory Unsafety in C and C++

Even today, over 70% of security vulnerabilities in critical software systems result from memory safety violations. To address this challenge, fuzzing and static analysis are widely used automated methods to discover such vulnerabilities. Fuzzing generates random program inputs to identify faults...

Jailbreak Distillation: Renewable Safety Benchmarking

Large language models LLMs are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation JBDistill, a novel benchmark construction framework that "distills" jailbreak attacks into high-quality and easily-updatable safety...

Securing the Software Package Supply Chain for Critical Systems

Software systems have grown as an indispensable commodity used across various industries, and almost all essential services depend on them for effective operation. The software is no longer an independent or stand-alone piece of code written by a developer but rather a collection of packages...

AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models

The acquisition of agentic capabilities has transformed LLMs from "knowledge providers" to "action executors", a trend that while expanding LLMs' capability boundaries, significantly increases their susceptibility to malicious use. Previous work has shown that current LLM-based agents execute...

Chainless Apps: a Modular Framework for Building Apps with Web2 Capability and Web3 Trust

Modern blockchain applications are often constrained by a trade-off between user experience and trust. Chainless Apps present a new paradigm of application architecture that separates execution, trust, bridging, and settlement into distinct compostable layers. This enables app-specific sequencing...

BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection

The current landscape of system-on-chips SoCs security verification faces challenges due to manual, labor-intensive, and inflexible methodologies. These issues limit the scalability and effectiveness of security protocols, making bug detection at the Register-Transfer Level RTL difficult. This...

Aurora: Are Android Malware Classifiers Reliable under Distribution Shift?

The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm primarily focuses on baseline performance metrics, neglecting confidence-error alignment and operational stability...

Efficient Preimage Approximation for Neural Network Certification

The growing reliance on artificial intelligence in safety- and security-critical applications demands effective neural network certification. A challenging real-world use case is certification against patch attacks'', where adversarial patches or lighting conditions obscure parts of images, for...

Private Rate-Constrained Optimization with Applications to Fair Learning

Many problems in trustworthy ML can be formulated as minimization of the model error under constraints on the prediction rates of the model for suitably-chosen marginals, including most group fairness constraints demographic parity, equality of odds, etc.. In this work, we study such constrained...

On the Intractability of Chaotic Symbolic Walks: toward a Non-Algebraic Post-Quantum Hardness Assumption

Most classical and post-quantum cryptographic assumptions, including integer factorization, discrete logarithms, and Learning with Errors LWE, rely on algebraic structures such as rings or vector spaces. While mathematically powerful, these structures can be exploited by quantum algorithms or...

BPMN to Smart Contract by Business Analyst

This paper addresses the challenge of creating smart contracts for applications represented using Business Process Management and Notation BPMN models. In our prior work we presented a methodology that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN...

GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance

DNA, encoding genetic instructions for almost all living organisms, fuels groundbreaking advances in genomics and synthetic biology. Recently, DNA Foundation Models have achieved success in designing synthetic functional DNA sequences, even whole genomes, but their susceptibility to jailbreaking...

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning

Privacy-Preserving Federated Learning PPFL is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves privacy and security of the client's data by not exchanging it. However, ensuring that data at each client is of high quality and ready for...

Private Lossless Multiple Release

Whitepaper called Private Lossless Multiple Release...

RedisBloom 2.6.12 Integer Overflow

There is an integer overflow vulnerability in RedisBloom version 2.6.12, which is a module used in redis. The integer overflow vulnerability allows an attacker a redis client which knows the password to allocate memory in the heap lesser than the required memory due to wraparound. Then read and...

Eve File Disclosure / Code Execution

Eve versions prior to 0.7.5 blind remote code execution proof of concept that retrieves files...

WordPress File Away 3.9.9.0.1 Arbitrary File Read

The File Away plugin for WordPress is vulnerable to unauthorized access of data due to a missing capability check on the ajax function in all versions up to, and including, 3.9.9.0.1. This makes it possible for unauthenticated attackers, leveraging the use of a reversible weak algorithm, to read...

WordPress Likes and Dislikes 1.0.0 SQL Injection

WordPress Likes and Dislikes plugin versions 1.0.0 and below suffer from an unauthenticated remote SQL injection vulnerability...

Machine Learning Models Have a Supply Chain Problem

Powerful machine learning ML models are now readily available online, which creates exciting possibilities for users who lack the deep technical expertise or substantial computing resources needed to develop them. On the other hand, this type of open ecosystem comes with many risks. In this paper...

Privacy-Preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models

Prompt learning is a crucial technique for adapting pre-trained multimodal language models MLLMs to user tasks. Federated prompt personalization FPP is further developed to address data heterogeneity and local overfitting, however, it exposes personalized prompts - valuable intellectual assets - ...

TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE

To safeguard user data privacy, on-device inference has emerged as a prominent paradigm on mobile and Internet of Things IoT devices. This paradigm involves deploying a model provided by a third party on local devices to perform inference tasks. However, it exposes the private model to two primar...

Privacy-Preserving Inconsistency Measurement

We investigate a new form of privacy-preserving inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases KA, KB of two agents A, B, our results allow to quantitatively assess the degree of inconsistency for KA U KB without having to reveal the actual contents ...

Security Benefits and Side Effects of Labeling AI-Generated Images

Generative artificial intelligence is developing rapidly, impacting humans' interaction with information and digital media. It is increasingly used to create deceptively realistic misinformation, so lawmakers have imposed regulations requiring the disclosure of AI-generated content. However, only...

Permissioned LLMs: Enforcing Access Control in Large Language Models

In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely break down if an LLM fine-tuned on the siloed data serves requests, for downstream tasks, from individuals with disparat...

VulBinLLM: LLM-Powered Vulnerability Detection for Stripped Binaries

Recognizing vulnerabilities in stripped binary files presents a significant challenge in software security. Although some progress has been made in generating human-readable information from decompiled binary files with Large Language Models LLMs, effectively and scalably detecting vulnerabilitie...

Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook

The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern...

Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment

CaMeL Capabilities for Machine Learning introduces a capability-based sandbox to mitigate prompt injection attacks in large language model LLM agents. While effective, CaMeL assumes a trusted user prompt, omits side-channel concerns, and incurs performance tradeoffs due to its dual-LLM design. Th...