CVE-2019-16553

A cross-site request forgery vulnerability in Jenkins Build Failure Analyzer Plugin 1.24.1 and earlier allows attackers to have Jenkins evaluate a computationally expensive regular expression...

CVE-2018-18758

Open Faculty Evaluation System 7 for PHP 7 allows submitfeedback.php SQL Injection, a different vulnerability than CVE-2018-18757...

Lantronix Device Installer

RISK EVALUATION Successful exploitation of this vulnerability could allow an attacker to gain access to the host machine running the Device Installer software. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability such...

CVE-2014-2686

Ansible prior to 1.5.4 mishandles the evaluation of some strings...

CVE-2018-18757

Open Faculty Evaluation System 5.6 for PHP 5.6 allows submitfeedback.php SQL Injection, a different vulnerability than CVE-2018-18758...

Password Strength Detection Via Machine Learning: Analysis, Modeling, and Evaluation

As network security issues continue gaining prominence, password security has become crucial in safeguarding personal information and network systems. This study first introduces various methods for system password cracking, outlines password defense strategies, and discusses the application of...

Harry Potter Is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models Via Automated Adversarial Prompting

This work presents LURK Latent UnleaRned Knowledge, a novel framework that probes for hidden retained knowledge in unlearned LLMs through adversarial suffix prompting. LURK automatically generates adversarial prompt suffixes designed to elicit residual knowledge about the Harry Potter domain, a...

LLM-BSCVM: an LLM-Based Blockchain Smart Contract Vulnerability Management Framework

Smart contracts are a key component of the Web 3.0 ecosystem, widely applied in blockchain services and decentralized applications. However, the automated execution feature of smart contracts makes them vulnerable to potential attacks due to inherent flaws, which can lead to severe security risks...

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression

Graph Neural Networks GNNs have been widely used for graph analysis. Federated Graph Learning FGL is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are required to upload model parameters to the server in each round, this provides t...

Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study

Rapid deployment of vision-language models VLMs magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted with meme images that ordinary users share? To investigate this question, we introduce MemeSafetyBench, a...

MAPS: a Multilingual Benchmark for Global Agent Performance and Security

Agentic AI systems, which build on Large Language Models LLMs and interact with tools and memory, have rapidly advanced in capability and scope. Yet, since LLMs have been shown to struggle in multilingual settings, typically resulting in lower performance and reduced safety, agentic systems risk...

Outsourcing SAT-Based Verification Computations in Network Security

The emergence of cloud computing gives huge impact on large computations. Cloud computing platforms offer servers with large computation power to be available for customers. These servers can be used efficiently to solve problems that are complex by nature, for example, satisfiability SAT problem...

Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities

Open-weight general-purpose AI GPAI models offer significant benefits but also introduce substantial cybersecurity risks, as demonstrated by the offensive capabilities of models like DeepSeek-R1 in evaluations such as MITRE's OCCULT. These publicly available models empower a wider range of actors...

National Instruments Circuit Design Suite

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to disclose information or execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this these vulnerabilities, such as: Minimize...

Mitsubishi Electric Iconics Digital Solutions and Mitsubishi Electric Products (Update F)

RISK EVALUATION Successful exploitation of this vulnerability could result in information tampering on the target workstation. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability, such as: Minimize network exposure for...

Evaluating the Efficacy of LLM Safety Solutions : the Palit Benchmark Dataset

Large Language Models LLMs are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives...

Lessons from Defending Gemini against Indirect Prompt Injections

Gemini is increasingly used to perform tasks on behalf of users, where function-calling and tool-use capabilities enable the model to access user data. Some tools, however, require access to untrusted data introducing risk. Adversaries can embed malicious instructions in untrusted data which caus...

Is Artificial Intelligence Generated Image Detection a Solved Problem?

The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image AIGI...

Vyper's `concat()` builtin may elide side-effects for zero-length arguments

Impact concat may skip evaluation of side effects when the length of an argument is zero. this is due to a fastpath in the implementation which skips evaluation of argument expressions when their length is zero:...

Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems Using Explainable AI

Federated Learning FL has emerged as a powerful paradigm for collaborative model training while keeping client data decentralized and private. However, it is vulnerable to Data Reconstruction Attacks DRA such as "LoKI" and "Robbing the Fed", where malicious models sent from the server to the clie...