router Administrator Panel Privilege Escalation

The vulnerability of the administrator panel of microprogrammed software routers such as GL-A1300, GL-AX1800, GL-AXT1800, GL-MT3000, GL-MT2500, GL-MT6000, GL-MT1300, GL-MT300N-V2, GL-AR750S, GL-AR750, GL-AR300M, and GL-B1300 is related to deficiencies in authentication procedures. Exploiting this...

JULI: Jailbreak Large Language Models by Self-Introspection

Large Language Models LLMs are trained with safety alignment to prevent generating malicious content. Although some attacks have highlighted vulnerabilities in these safety-aligned LLMs, they typically have limitations, such as necessitating access to the model weights or the generation process...

Is Your Prompt Safe? Investigating Prompt Injection Attacks against Open-Source LLMs

Whitepaper called Is Your Prompt Safe? Investigating Prompt Injection Attacks Against Open-Source LLMs...

Training-Free Watermarking for Autoregressive Image Generation

Invisible image watermarking can protect image ownership and prevent malicious misuse of visual generative models. However, existing generative watermarking methods are mainly designed for diffusion models while watermarking for autoregressive image generation models remains largely underexplored...

FedGraM: Defending against Untargeted Attacks in Federated Learning Via Embedding Gram Matrix

Federated Learning FL enables geographically distributed clients to collaboratively train machine learning models by sharing only their local models, ensuring data privacy. However, FL is vulnerable to untargeted attacks that aim to degrade the global model's performance on the underlying data...

AudioJailbreak: Jailbreak Attacks against End-To-End Large Audio-Language Models

Jailbreak attacks to Large audio-language models LALMs are studied recently, but they achieve suboptimal effectiveness, applicability, and practicability, particularly, assuming that the adversary can fully manipulate user prompts. In this work, we first conduct an extensive experiment showing th...

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents

The growing adoption of large language models LLMs has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex tasks directly on smartphones. However, the security implications of these agents remain largely unexplored. In this paper,...

Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs

In the age of cloud computing, data privacy protection has become a major challenge, especially when sharing sensitive data across cloud environments. However, how to optimize collaboration across cloud environments remains an unresolved problem. In this paper, we combine federated learning with...

Fragments to Facts: Partial-Information Fragment Inference from LLMs

Large language models LLMs can leak sensitive training data through memorization and membership inference attacks. Prior work has primarily focused on strong adversarial assumptions, including attacker access to entire samples or long, ordered prefixes, leaving open the question of how vulnerable...

MorphMark: Flexible Adaptive Watermarking for Large Language Models

Watermarking by altering token sampling probabilities based on red-green list is a promising method for tracing the origin of text generated by large language models LLMs. However, existing watermark methods often struggle with a fundamental dilemma: improving watermark effectiveness the...

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems

Large Language Models LLMs enhanced with Retrieval-Augmented Generation RAG have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly...

R1dacted: Investigating Local Censorship in DeepSeek'S R1 Language Model

DeepSeek recently released R1, a high-performing large language model LLM optimized for reasoning tasks. Despite its efficient training pipeline, R1 achieves competitive performance, even surpassing leading reasoning models like OpenAI's o1 on several benchmarks. However, emerging reports suggest...

TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text

Accurately identifying adversarial techniques in security texts is critical for effective cyber defense. However, existing methods face a fundamental trade-off: they either rely on generic models with limited domain precision or require resource-intensive pipelines that depend on large labeled...

Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hunting

As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect and mitigate risks. Large Language Models LLMs offer promising capabilities for enhancing threat analysis. However, their effectiveness in real-world blue...

FABLE: a Localized, Targeted Adversarial Attack on Weather Forecasting Models

Deep learning-based weather forecasting models have recently demonstrated significant performance improvements over gold-standard physics-based simulation tools. However, these models are vulnerable to adversarial attacks, which raises concerns about their trustworthiness. In this paper, we first...

On Membership Inference Attacks in Knowledge Distillation

Nowadays, Large Language Models LLMs are trained on huge datasets, some including sensitive information. This poses a serious privacy concern because privacy attacks such as Membership Inference Attacks MIAs may detect this sensitive information. While knowledge distillation compresses LLMs into...

com.alibaba.citrus:citrus-springext-all (>=3.1.6, <=3.2.4), com.alibaba.citrus:citrus-test-all (>=3.1.6, <=3.2.4) +63 more potentially affected by CVE-2025-22233 via org.springframework:spring-context (3.2.7.release)

org.springframework:spring-context MAVEN version 3.2.7.release is affected by a known vulnerability. The following packages have a transitive dependency on org.springframework:spring-context and may be impacted: - com.alibaba.citrus:citrus-springext-all =3.1.6, =3.1.6, =3.1.6, =0.10.0, =1.0,...

CVE-2024-56427

An issue was discovered in Samsung Mobile Processor and Wearable Processor Exynos 980, 990, 850, 1080, 2100, 1280, 2200, 1330, 1380, 1480, 2400, 9110, W920, W930, W1000, Modem 5123, Modem 5300, Modem 5400. The lack of a length check leads to out-of-bounds access via malformed RRC packets to the...

ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks

The integration of large language models LLMs into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing and domain expertise. These prompts enhance task performance but may also encode sensitive information and filtering...

Diverging Towards Hallucination: Detection of Failures in Vision-Language Models Via Multi-Token Aggregation

Vision-language models VLMs now rival human performance on many multimodal tasks, yet they still hallucinate objects or generate unsafe text. Current hallucination detectors, e.g., single-token linear probing SLP and PTrue, typically analyze only the logit of the first generated token or just its...