What Hackers Talk about When They Talk about AI: Early-Stage Diffusion of a Cybercrime Innovation

The rapid expansion of artificial intelligence AI is raising concerns about its potential to transform cybercrime. Beyond empowering novice offenders, AI stands to intensify the scale and sophistication of attacks by seasoned cybercriminals. This paper examines the evolving relationship between...

AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises

Today's leading AI models engage in sophisticated behaviour when placed in strategic competition. They spontaneously attempt deception, signaling intentions they do not intend to follow; they demonstrate rich theory of mind, reasoning about adversary beliefs and anticipating their actions; and th...

Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks

As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight models must primarily depend on internal safeguards to mitigate harmful behavior. Prior red-teaming research has largel...

Google Ties Suspected Russian Actor to CANFAIL Malware Attacks on Ukrainian Orgs

A previously undocumented threat actor has been attributed to attacks targeting Ukrainian organizations with malware known as CANFAIL. Google Threat Intelligence Group GTIG described the hacking group as possibly affiliated with Russian intelligence services. The threat actor is assessed to have...

In-Context Autonomous Network Incident Response: An End-To-End Large Language Model Agent Approach

Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively explored the reinforcement learning approach, which involves learning response strategies through extensive simulation of the incident. While this...

Sparse Autoencoders Are Capable LLM Jailbreak Mitigators

Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering CC-Delta, an SAE-based defense that identifies jailbreak-relevant sparse features by comparing token-level representations of the same harmful request with and without...

Automatic Simplification of Common Vulnerabilities and Exposures Descriptions

Understanding cyber security is increasingly important for individuals and organizations. However, a lot of information related to cyber security can be difficult to understand to those not familiar with the topic. In this study, we focus on investigating how large language models LLMs could be...

An Empirical Study of the Imbalance Issue in Software Vulnerability Detection

Vulnerability detection is crucial to protect software security. Nowadays, deep learning DL is the most promising technique to automate this detection task, leveraging its superior ability to extract patterns and representations within extensive code volumes. Despite its promise, DL-based...

AMD Processors 安全漏洞

AMD Processors are a series of processors developed by American semiconductor company AMD. There is a security vulnerability in AMD Processors, which stems from type confusion. This vulnerability may allow attackers to send malformed parameters to external global memory interconnection trusted...

GoodVibe: Security-By-Vibe for LLM-Based Code Generation

Large language models LLMs are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are prioritized, and security requirements are rarely made explicit. In this setting, models frequently produce functionally...

Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models

Jailbreaking large language models LLMs has emerged as a critical security challenge with the widespread deployment of conversational AI systems. Adversarial users exploit these models through carefully crafted prompts to elicit restricted or unsafe outputs, a phenomenon commonly referred to as...

Vulnerabilities in Partial TEE-Shielded LLM Inference with Precomputed Noise

The deployment of large language models LLMs on third-party devices requires new ways to protect model intellectual property. While Trusted Execution Environments TEEs offer a promising solution, their performance limits can lead to a critical compromise: using a precomputed, static secret basis ...

TRACE: Timely Retrieval and Alignment for Cybersecurity Knowledge Graph Construction and Expansion

The rapid evolution of cyber threats has highlighted significant gaps in security knowledge integration. Cybersecurity Knowledge Graphs CKGs relying on structured data inherently exhibit hysteresis, as the timely incorporation of rapidly evolving unstructured data remains limited, potentially...

Keras 安全漏洞

Keras is an open-source deep learning framework with multiple backends. Versions of Keras 3.13.1 and earlier contain security vulnerabilities. These vulnerabilities stem from defects in the model loading mechanism HDF5 integration, which could allow remote attackers to read local files through...

CVE-2026-26013

LangChain is a framework for building agents and LLM-powered applications. Prior to 1.2.11, the ChatOpenAI.getnumtokensfrommessages method fetches arbitrary imageurl values without validation when computing token counts for vision-enabled models. This allows attackers to trigger Server-Side Reque...

CVE-2026-26013 LangChain affected by SSRF via image_url token counting in ChatOpenAI.get_num_tokens_from_messages

LangChain is a framework for building agents and LLM-powered applications. Prior to 1.2.11, the ChatOpenAI.getnumtokensfrommessages method fetches arbitrary imageurl values without validation when computing token counts for vision-enabled models. This allows attackers to trigger Server-Side Reque...

CVE-2026-26013 LangChain affected by SSRF via image_url token counting in ChatOpenAI.get_num_tokens_from_messages

LangChain is a framework for building agents and LLM-powered applications. Prior to 1.2.11, the ChatOpenAI.getnumtokensfrommessages method fetches arbitrary imageurl values without validation when computing token counts for vision-enabled models. This allows attackers to trigger Server-Side Reque...

The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

A one-prompt attack that breaks LLM safety alignment

Large language models LLMs and diffusion models now power a wide range of applications, from document assistance to text-to-image generation, and users increasingly expect these systems to be safety-aligned by default. Yet safety alignment is only as robust as its weakest failure mode. Despite...

AI chat app leak exposes 300 million messages tied to 25 million users

An independent security researcher uncovered a major data breach affecting Chat & Ask AI, one of the most popular AI chat apps on Google Play and Apple App Store, with more than 50 million users. The researcher claims to have accessed 300 million messages from over 25 million users due to an...