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...

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...

Which Factors Make Code LLMs More Vulnerable to Backdoor Attacks? A Systematic Study

Code LLMs are increasingly employed in software development. However, studies have shown that they are vulnerable to backdoor attacks: when a trigger a specific input pattern appears in the input, the backdoor will be activated and cause the model to generate malicious outputs. Researchers have...

Security Concerns for Large Language Models: a Survey

Large Language Models LLMs such as GPT-4 and its recent iterations, Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive...

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges

The rapid adoption of machine learning ML technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations MLOps has emerged as an integrative approach addressing these requirements by...

Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation RAG systems, which integrate Large Language Models LLMs with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent...

A Linear Approach to Data Poisoning

We investigate the theoretical foundations of data poisoning attacks in machine learning models. Our analysis reveals that the Hessian with respect to the input serves as a diagnostic tool for detecting poisoning, exhibiting spectral signatures that characterize compromised datasets. We use rando...

Securing Generative AI: Navigating Risk and Building Resilience

Running short on time but still want to stay in the know? Well, we’ve got you covered! We’ve condensed all the key takeaways into a handy audio summary. Our AI-driven podcasts are fit for on the go. Click right here to hear it all on CAASM & CDMB Inefficiencies! Generative AI has changed the way ...

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?

Low rank adaptation LoRA has emerged as a prominent technique for fine-tuning large language models LLMs thanks to its superb efficiency gains over previous methods. While extensive studies have examined the performance and structural properties of LoRA, its behavior upon training-time attacks...

Sybil-Based Virtual Data Poisoning Attacks in Federated Learning

Federated learning is vulnerable to poisoning attacks by malicious adversaries. Existing methods often involve high costs to achieve effective attacks. To address this challenge, we propose a sybil-based virtual data poisoning attack, where a malicious client generates sybil nodes to amplify the...

Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted

With the advancement of AI generative techniques, Deepfake faces have become incredibly realistic and nearly indistinguishable to the human eye. To counter this, Deepfake detectors have been developed as reliable tools for assessing face authenticity. These detectors are typically developed on De...

Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can...

A Chaos Driven Metric for Backdoor Attack Detection

The advancement and adoption of Artificial Intelligence AI models across diverse domains have transformed the way we interact with technology. However, it is essential to recognize that while AI models have introduced remarkable advancements, they also present inherent challenges such as their...

Investigating Cybersecurity Incidents Using Large Language Models in Latest-Generation Wireless Networks

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems,...

AI Data Poisoning

Cloudflare has a new feature--available to free users as well--that uses AI to generate random pages to feed to AI web crawlers: Instead of simply blocking bots, Cloudflare's new system lures them into a "maze" of realistic-looking but irrelevant pages, wasting the crawler's computing resources...

CVE-2023-50943

Apache Airflow, versions before 2.8.1, have a vulnerability that allows a potential attacker to poison the XCom data by bypassing the protection of "enablexcompickling=False" configuration setting resulting in poisoned data after XCom deserialization. This vulnerability is considered low since it...

Securing AI Development in the Cloud: Navigating the Risks and Opportunities

AI-TRiSM - Trust, Risk and Security Management in the Age of AI Co-authored by Lara Sunday and Pojan Shahrivar As artificial intelligence AI and machine learning ML technologies continue to advance and proliferate, organizations across industries are investing heavily in these transformative...

CVE-2024-5185

The EmbedAI application is susceptible to security issues that enable Data Poisoning attacks. This weakness could result in the application becoming compromised, leading to unauthorized entries or data poisoning attacks, which are delivered by a CSRF vulnerability due to the absence of a secure...

CVE-2024-5185 Data Poisoning in EmbedAI

The EmbedAI application is susceptible to security issues that enable Data Poisoning attacks. This weakness could result in the application becoming compromised, leading to unauthorized entries or data poisoning attacks, which are delivered by a CSRF vulnerability due to the absence of a secure...

CVE-2024-5185

CVE-2024-5185 concerns the EmbedAI application, where a CSRF weakness resulting from the absence of secure session management and weak CORS policies enables data poisoning. An attacker can lure a user to a malicious page that triggers the CSRF flaw, causing the user to upload and integrate incorr...