CVE-2026-82275

Qwen-Agent through 0.0.34 contains a path traversal vulnerability in the document parser that fails to restrict file access to intended directories. Attackers can supply absolute file paths to the unauthenticated Gradio interface to read arbitrary files accessible by the server process...

CVE-2026-82268

Qwen-Agent through 0.0.34 contains a server-side request forgery vulnerability in the document parsing path that treats caller-supplied paths as URLs without scheme restriction or host validation. Attackers can reach the unauthenticated Gradio interface to make the server issue HTTP requests to...

EUVD-2026-67806

Qwen-Agent through 0.0.34 contains a path traversal vulnerability in the document parser that fails to restrict file access to intended directories. Attackers can supply absolute file paths to the unauthenticated Gradio interface to read arbitrary files accessible by the server process...

CVE-2026-82275 Qwen-Agent Arbitrary File Read via Caller-Supplied Document Path

Qwen-Agent through 0.0.34 contains a path traversal vulnerability in the document parser that fails to restrict file access to intended directories. Attackers can supply absolute file paths to the unauthenticated Gradio interface to read arbitrary files accessible by the server process...

CVE-2026-82275

Qwen-Agent through 0.0.34 contains a path traversal vulnerability in its document parser (in simple_doc_parser.py and utils.py) that fails to restrict file access to intended directories. Because the Gradio interface is unauthenticated, an attacker can supply absolute file paths to read arbitrary...

CVE-2026-82268 Qwen-Agent Server-Side Request Forgery via Caller-Supplied Document URL

Qwen-Agent through 0.0.34 contains a server-side request forgery vulnerability in the document parsing path that treats caller-supplied paths as URLs without scheme restriction or host validation. Attackers can reach the unauthenticated Gradio interface to make the server issue HTTP requests to...

EUVD-2026-67799

Qwen-Agent through 0.0.34 contains a server-side request forgery vulnerability in the document parsing path that treats caller-supplied paths as URLs without scheme restriction or host validation. Attackers can reach the unauthenticated Gradio interface to make the server issue HTTP requests to...

CVE-2026-82268

Qwen-Agent through 0.0.34 contains a server-side request forgery (SSRF) vulnerability in its document parsing path (qwen_agent/tools/simple_doc_parser.py). Caller-supplied paths are treated as URLs without scheme restriction or host validation , allowing an unauthenticated attacker to reach the G...

PT-2026-67349

Name of the Vulnerable Software and Affected Versions huggingface/transformers versions prior to 5.8.0.dev0 Description An issue allows arbitrary file writes through path traversal, a technique used to access files and directories outside the intended folder. The flaw exists in the save pretraine...

GHSA-8WR5-JM2H-8R4F vLLM has Remote DoS via Invalid Recovered Token Reinjection

Summary A frontend-legal multi-request speculative workload can make vLLM produce an out-of-vocabulary recovered token equal to vocabsize, convert that value to -1 when choosing the next live token for a request, and then feed that -1 back into the next drafter input ids. On Qwen3 GPTQ this reach...

RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks

Reasoning-capable large language models can be induced to spend their generation budget on injected decoy tasks rather than answering the user's question, causing denial of service when no final answer is produced and denial of wallet when excess output tokens are billed. Input-side safety...

@activepieces/piece-ai (>=0.3.1, <=0.3.4), @middy/validator (>=5.4.3, <=5.4.4) +4 more potentially affected by CVE-2026-6322 via fast-uri (>=3.0.1, <=3.1.0)

fast-uri NPM version =3.0.1, =0.3.1, =5.4.3, =1.0.0, =1.0.0, =2.2.0, =2.3.1 Source cves: CVE-2026-6322 Source advisory: OSV:GHSA-V39H-62P7-JPJC...

Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis

Cyber threats are rapidly increasing, expanding their impact from large-scale enterprises to government services and individual users, making robust security systems increasingly essential. However, a significant shortage of skilled cybersecurity professionals exacerbates this challenge. While...

PT-2026-37318

Name of the Vulnerable Software and Affected Versions vLLM versions 0.6.1 through 0.19.x Description A Token Injection issue exists in the multimodal processing of vLLM. Unauthenticated, text-only prompts containing special tokens are interpreted as control commands. When image and video...

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts

The deployment of large language models LLMs in Swiss financial and regulatory contexts demands empirical evidence of both production reliability and adversarial security, dimensions not jointly operationalized in existing Swiss-focused evaluation frameworks. This paper introduces Swiss-Bench 003...

Analysis of LLMs against Prompt Injection and Jailbreak Attacks

Large Language Models LLMs are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient tool for resource-scarce organizations to adopt LLMs for their own purposes. At the same time, LLMs are vulnerable to prompt-based attacks. Thus,...

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...

Evaluating and Enhancing the Vulnerability Reasoning Capabilities of Large Language Models

Large Language Models LLMs have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists: models frequently yield correct detection verdicts based on hallucinated logic or superficial patterns that deviate from the actual root cause. This...

Emoji-Based Jailbreaking of Large Language Models

Large Language Models LLMs are integral to modern AI applications, but their safety alignment mechanisms can be bypassed through adversarial prompt engineering. This study investigates emoji-based jailbreaking, where emoji sequences are embedded in textual prompts to trigger harmful and unethical...

Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs

Backdoor attacks embed malicious behaviors into Large Language Models LLMs, enabling adversaries to trigger harmful outputs or bypass safety controls. However, the persistence of the implanted backdoors under user-driven post-deployment continual fine-tuning has been rarely examined. Most prior...