Can We End the Cat-And-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms

Anticipating emerging attack methodologies is crucial for proactive cybersecurity. Recent advances in Large Language Models LLMs have enabled the automated generation of phishing messages and accelerated research into potential attack techniques. However, predicting future threats remains...

Secure Coding for Web Applications: Frameworks, Challenges, and the Role of LLMs

Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organizational, educational, and technical barriers. This paper provides a comprehensive review of secure coding practices across...

Securing Cloud AI and LLMs with TotalAI for Visibility, Risk Context and Control

As enterprises accelerate AI adoption, large language models LLMs hosted on public cloud platforms are quickly becoming the norm due to their simplified access and pricing model. Cloud-native services like AWS Bedrock, Azure AI Foundry, and Google Vertex AI offer powerful, pay-as-you-go access to...

The vulnerability of the framework for working with large language models (LLMs) like LlamaIndex lies in the improper restriction on recursive references to entities in the DTD. This allows attackers to trigger a service failure.

The vulnerability of the LlamaIndex framework for working with large language models is related to an improper limitation on recursive references to entities in the DTD. Exploiting this vulnerability could allow a malicious actor to cause service failures...

The vulnerability of Ollama’s system for running and managing large language models lies in its lack of proper input data validation, allowing attackers to execute arbitrary code.

The vulnerability of Ollama’s system for running and managing large language models is related to insufficient validation of input data. Exploiting this vulnerability could allow a remote attacker to execute arbitrary code...

Subliminal Learning in AIs

Today's freaky LLM behavior: We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a...

PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

Static benchmarks fail to capture LLM vulnerabilities emerging through community experimentation in online forums. We present PrompTrend, a system that collects vulnerability data across platforms and evaluates them using multidimensional scoring, with an architecture designed for scalable...

OneShield -- the Next Generation of LLM Guardrails

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective...

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery

Recent technological advancements and the prevalence of technology in day to day activities have caused a major increase in the likelihood of the involvement of digital evidence in more and more legal investigations. Consumer-grade hardware is growing more powerful, with expanding memory and...

Enabling Cyber Security Education through Digital Twins and Generative AI

Digital Twins DTs are gaining prominence in cybersecurity for their ability to replicate complex IT Information Technology, OT Operational Technology, and IoT Internet of Things infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how...

EX-NIDS: a Framework for Explainable Network Intrusion Detection Leveraging Large Language Models

This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems NIDS by leveraging Large Language Models LLMs. In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Promp...

Talking like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers

Voice phishing vishing remains a persistent threat in cybersecurity, exploiting human trust through persuasive speech. While machine learning ML-based classifiers have shown promise in detecting malicious call transcripts, they remain vulnerable to adversarial manipulations that preserve semantic...

LLMxCPG: Context-Aware Vulnerability Detection through Code Property Graph-Guided Large Language Models

Software vulnerabilities present a persistent security challenge, with over 25,000 new vulnerabilities reported in the Common Vulnerabilities and Exposures CVE database in 2024 alone. While deep learning based approaches show promise for vulnerability detection, recent studies reveal critical...

How Search Engines, LLMs, and Third-Party Scrapers Affect Bot Management

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SynthCTI: LLM-Driven Synthetic CTI Generation to Enhance MITRE Technique Mapping

Cyber Threat Intelligence CTI mining involves extracting structured insights from unstructured threat data, enabling organizations to understand and respond to evolving adversarial behavior. A key task in CTI mining is mapping threat descriptions to MITRE ATT&CK techniques. However, this process...

In-Context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems

Recent advances in biometric systems have significantly improved the detection and prevention of fraudulent activities. However, as detection methods improve, attack techniques become increasingly sophisticated. Attacks on face recognition systems can be broadly divided into physical and digital...

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

Large Language Models LLMs are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in...

Introducing Akamai Cloud Pulse: Observability for Your Cloud Infrastructure – Now in Open Beta

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DOGE Denizen Marko Elez Leaked API Key for xAI

Marko Elez , a 25-year-old employee at Elon Musk's Department of Government Efficiency DOGE, has been granted access to sensitive databases at the U.S. Social Security Administration, the Treasury and Justice departments, and the Department of Homeland Security. So it should fill all Americans wi...

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design

The integration of Large Language Models LLMs in K--12 education offers both transformative opportunities and emerging risks. This study explores how students may Trojanize prompts to elicit unsafe or unintended outputs from LLMs, bypassing established content moderation systems with safety...