Code Vulnerability Detection across Different Programming Languages with AI Models

Security vulnerabilities present in a code that has been written in diverse programming languages are among the most critical yet complicated aspects of source code to detect. Static analysis tools based on rule-based patterns usually do not work well at detecting the context-dependent bugs and...

BERTector: Intrusion Detection Based on Joint-Dataset Learning

Intrusion detection systems IDS are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based...

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection

Ensuring LLM alignment is critical to information security as AI models become increasingly widespread and integrated in society. Unfortunately, many defenses against adversarial attacks and jailbreaking on LLMs cannot adapt quickly to new attacks, degrade model responses to benign prompts, or...

PT-2025-32492 · Pypi · Ms-Swift

I. Detailed Description: 1. Install ms-swift pip install ms-swift -U 2. Start web-ui swift web-ui --lang en 3. After startup, access through browser at http://localhost:7860/ to see the launched fine-tuning framework program 4. Fill in necessary parameters In the LLM Training interface, fill in...

org.keycloak/keycloak-services: Privilege Escalation in Keycloak Admin Console (FGAPv2 Enabled)

A flaw was found in the Keycloak identity and access management system when Fine-Grained Admin PermissionsFGAPv2 are enabled. An administrative user with the manage-users role can escalate their privileges to realm-admin due to improper privilege enforcement. This vulnerability allows unauthorize...

SDD: Self-Degraded Defense against Malicious Fine-Tuning

Open-source Large Language Models LLMs often employ safety alignment methods to resist harmful instructions. However, recent research shows that maliciously fine-tuning these LLMs on harmful data can easily bypass these safeguards. To counter this, we theoretically uncover why malicious fine-tuni...

Privilege Escalation

org.keycloak, keycloak-services is vulnerable to privilege escalation. The vulnerability is due to improper privilege enforcement when Fine-Grained Admin Permissions FGAPv2 are enabled, which allows an attacker with the manage-users role to escalate privileges to realm-admin...

LoRA-Leak: Membership Inference Attacks against LoRA Fine-Tuned Language Models

Language Models LMs typically adhere to a "pre-training and fine-tuning" paradigm, where a universal pre-trained model can be fine-tuned to cater to various specialized domains. Low-Rank Adaptation LoRA has gained the most widespread use in LM fine-tuning due to its lightweight computational cost...

Scaling Decentralized Learning with FLock

Fine-tuning the large language models LLMs are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning FL supports data privacy, the central server requirement creates a...

Duplicate Advisory: Keycloak Privilege Escalation Vulnerability in Admin Console (FGAPv2 Enabled)

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-27gp-8389-hm4w. This link is maintained to preserve external references. Original Description A flaw was found in the Keycloak identity and access management system when Fine-Grained Admin Permissions FGAPv2 are...

CVE-2025-7784 Org.keycloak/keycloak-services: privilege escalation in keycloak admin console (fgapv2 enabled)

A flaw was found in the Keycloak identity and access management system when Fine-Grained Admin PermissionsFGAPv2 are enabled. An administrative user with the manage-users role can escalate their privileges to realm-admin due to improper privilege enforcement. This vulnerability allows unauthorize...

CVE-2025-7784 Org.keycloak/keycloak-services: privilege escalation in keycloak admin console (fgapv2 enabled)

A flaw was found in the Keycloak identity and access management system when Fine-Grained Admin PermissionsFGAPv2 are enabled. An administrative user with the manage-users role can escalate their privileges to realm-admin due to improper privilege enforcement. This vulnerability allows unauthorize...

CVE-2025-7784 Org.keycloak/keycloak-services: privilege escalation in keycloak admin console (fgapv2 enabled)

A flaw was found in the Keycloak identity and access management system when Fine-Grained Admin PermissionsFGAPv2 are enabled. An administrative user with the manage-users role can escalate their privileges to realm-admin due to improper privilege enforcement. This vulnerability allows unauthorize...

CVE-2025-7784

CVE-2025-7784 - Keycloak FGAPv2 Privilege Escalation This entry describes a privilege-escalation vulnerability in Keycloak when Fine-Grained Admin Permissions (FGAPv2) are enabled. An administrative user who holds the manage-users role can elevate themselves to realm-admin due to improper privile...

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility

AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates that fine-tuning, whether via open weights or closed fine-tuning APIs, can produce helpful-only models. In contrast to...

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

Automated Program Repair APR is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of...

May I Have Your Attention? Breaking Fine-Tuning Based Prompt Injection Defenses Using Architecture-Aware Attacks

A popular class of defenses against prompt injection attacks on large language models LLMs relies on fine-tuning the model to separate instructions and data, so that the LLM does not follow instructions that might be present with data. There are several academic systems and production-level...

TuneShield: Mitigating Toxicity in Conversational AI While Fine-Tuning on Untrusted Data

Recent advances in foundation models, such as LLMs, have revolutionized conversational AI. Chatbots are increasingly being developed by customizing LLMs on specific conversational datasets. However, mitigating toxicity during this customization, especially when dealing with untrusted training dat...

SV-LLM: an Agentic Approach for SoC Security Verification Using Large Language Models

Ensuring the security of complex system-on-chips SoCs designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models LLMs, with their...

Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA

Memorization in large language models LLMs makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method. In this...