CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents

LLM-based code interpreter agents are increasingly deployed in critical workflows, yet their robustness against risks introduced by their code execution capabilities remains underexplored. Existing benchmarks are limited to static datasets or simulated environments, failing to capture the securit...

LLM-Enabled Applications Require System-Level Threat Monitoring

LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. This paradigm shift, however, introduces fundamentally new reliability challenges and significantly expands the security attack surface, du...

CVE-2026-20137

In Splunk Enterprise versions below 10.2.0, 10.0.3, 9.4.5, 9.3.7, and 9.2.9, and Splunk Cloud Platform versions below 10.1.2507.0, 10.0.2503.9, 9.3.2411.112, and 9.3.2408.122, a low-privileged user who does not hold the "admin" or "power" Splunk roles could bypass the SPL safeguards for risky...

TFL: Targeted Bit-Flip Attack on Large Language Model

Large language models LLMs are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks BFAs, which exploit computer main memory i.e., DRAM vulnerabilitie...

Would You Click ‘Accept’? Automatically detecting malicious Azure OAuth applications using LLMs

How Wiz Research automates detection of emerging malicious Azure app and consent phishing campaigns...

Recursive Language Models for Jailbreak Detection: A Procedural Defense for Tool-Augmented Agents

Jailbreak prompts are a practical and evolving threat to large language models LLMs, particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic camouflage, and lightweight obfuscations that can evade single-pass guardrails. We...

Mind the Gap: Evaluating LLMs for High-Level Malicious Package Detection Vs. Fine-Grained Indicator Identification

The prevalence of malicious packages in open-source repositories, such as PyPI, poses a critical threat to the software supply chain. While Large Language Models LLMs have emerged as a promising tool for automated security tasks, their effectiveness in detecting malicious packages and indicators...

PT-2026-20432

Name of the Vulnerable Software and Affected Versions: Grandstream GXP1610, GXP1615, GXP1620, GXP1625, GXP1628, and GXP1630 versions prior to 1.0.7.81. Description: A critical unauthenticated stack-based buffer overflow vulnerability exists in the HTTP API endpoint /cgi-bin/api.values.get. This...

Discovering Universal Activation Directions for PII Leakage in Language Models

Modern language models exhibit rich internal structure, yet little is known about how privacy-sensitive behaviors, such as personally identifiable information PII leakage, are represented and modulated within their hidden states. We present UniLeak, a mechanistic-interpretability framework that...

Grandstream GXP series 安全漏洞

The Grandstream GXP series is a series of IP phones produced by the American company Grandstream. There are security vulnerabilities in the Grandstream GXP series. These vulnerabilities stem from an unauthenticated, stack-based buffer overflow vulnerability in the /cgi-bin/api.values.get HTTP API...

Side-Channel Attacks Against LLMs

Here are three papers describing different side-channel attacks against LLMs. "Remote Timing Attacks on Efficient Language Model Inference": Abstract: Scaling up language models has significantly increased their capabilities. But larger models are slower models, and so there is now an extensive...

DARTH-PUM: A Hybrid Processing-Using-Memory Architecture

Analog processing-using-memory PUM; a.k.a. in-memory computing makes use of electrical interactions inside memory arrays to perform bulk matrix-vector multiplication MVM operations. However, many popular matrix-based kernels need to execute non-MVM operations, which analog PUM cannot directly...

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...

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...

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...

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...