CVE-2025-62155

New API is a large language mode LLM gateway and artificial intelligence AI asset management system. Prior to version 0.9.6, a recently patched SSRF vulnerability contains a bypass method that can bypass the existing security fix and still allow SSRF to occur. Because the existing fix only applie...

Malicious code in chat-prompt-logger (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 f25a736985f5c0bb50156fdc7de61e976b16416f42c44a2682b5ce718401383b The package provides a logger of LLM prompts that at the same time looks for hidden instructions and executes them. --- Category: MALICIOUS - The campaign has...

CVE-2025-33204

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

NVIDIA Nemo Framework 代码注入漏洞

NVIDIA Nemo Framework is a framework for building and deploying generative AI models from NVIDIA. A code injection vulnerability exists in NVIDIA Nemo Framework that stems from the presence of malicious data in the NLP and LLM components, which could lead to code injection that could result in co...

Effective Command-Line Interface Fuzzing with Path-Aware Large Language Model Orchestration

Command-line interface CLI fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of vulnerabilities that only manifest under specific option-input combinations. Prior works of CLI fuzzing face the challenges of generating semantics-rich opti...

CVE-2025-62155

The CVE-2025-62155 entry concerns QuantumNous/new-api. A SSRF vulnerability existed prior to version 0.9.6 where the fix only protected the first URL request; an attacker could bypass via a 302 redirect and reach internal/intranet resources. The issue has been addressed in version 0.9.6, accordin...

DUALGUAGE: Automated Joint Security-Functionality Benchmarking for Secure Code Generation

Large language models LLMs and autonomous coding agents are increasingly used to generate software across a wide range of domains. Yet a core requirement remains unmet: ensuring that generated code is secure without compromising its functional correctness. Existing benchmarks and evaluations for...

PT-2025-47975

Name of the Vulnerable Software and Affected Versions New API versions prior to 0.9.6 Description New API is a large language model LLM gateway and artificial intelligence AI asset management system. A Server-Side Request Forgery SSRF condition existed in versions prior to 0.9.6. A previous...

AI teddy bear for kids responds with sexual content and advice about weapons

In testing, FoloToy’s AI teddy bear jumped from friendly chat to sexual topics and unsafe household advice. It shows how easily artificial intelligence can cross serious boundaries. It’s a fair moment to ask whether AI-powered stuffed animals are appropriate for children. It’s easy to get swept u...

CVE-2025-62426

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the...

EUVD-2025-198357

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape e.g. hidden dimension is wrong, regardless of whether...

EUVD-2025-198356

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the...

EUVD-2025-198314

vLLM is an inference and serving engine for large language models LLMs. From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash denial-of-service and potentially remote code execution RCE, exists in the Completions API endpoint. When processing user-supplied...

vLLM 安全漏洞

vLLM is a high throughput and memory efficient inference and service engine for LLM from vLLM open source. A security vulnerability exists in vLLM version 0.5.5 through versions prior to 0.11.1, which stems from insufficient validation of the chattemplatekwargs parameter, and may result in API...

PT-2025-47650

Name of the Vulnerable Software and Affected Versions vLLM versions 0.5.5 through 0.11.0 Description vLLM is an inference and serving engine for large language models LLMs. The /v1/chat/completions and /tokenize API endpoints accept a chat template kwargs request parameter that is not properly...

MalRAG: A Retrieval-Augmented LLM Framework for Open-Set Malicious Traffic Identification

Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious traffic a critical necessity as well as the identification of known classes. Recent studies have advanced this goal wi...

LogPurge: Log Data Purification for Anomaly Detection Via Rule-Enhanced Filtering

Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detectio...

RulePilot: An LLM-Powered Agent for Security Rule Generation

The real-time demand for system security leads to the detection rules becoming an integral part of the intrusion detection life-cycle. Rule-based detection often identifies malicious logs based on the predefined grammar logic, requiring experts with deep domain knowledge for rule generation...

Robustness of LLM-Enabled Vehicle Trajectory Prediction under Data Security Threats

The integration of large language models LLMs into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent studies demonstrate that fine-tuned LLMs can accurately predict...

LLM Response Evaluation with Spring AI: Building LLM-as-a-Judge Using Recursive Advisors

The challenge of evaluating Large Language Model LLM outputs is critical for notoriously non-deterministic AI applications, especially as they move into production. Traditional metrics like ROUGE and BLEU fall short when assessing the nuanced, contextual responses that modern LLMs produce. Human...