aa-rag (>=0.4.2, <=0.4.3), aaa-ml-datasets-course (1.0.0) +801 more potentially affected by CVE-2026-0848 via nltk (>=2.0.4, <=3.9.2)

nltk PYPI version =2.0.4, =0.4.2, =0.2.3, =0.2.0, =0.0.4, =0.0.1, =0.1.0, =0.1.0, =0.0.9, =0.17.0, =0.0.0.dev0, =7.28.2.post5.dev33128883500 and more Source cves: CVE-2026-0848 Source advisory: MEND:CVE-2026-0848...

Recovery-Induced Erasure Attack on QKD Systems

Detector dead time is typically treated as a fixed parameter in quantum key distribution QKD security analyses. In practice, however, the effective recovery time of single-photon avalanche photodiodes SPADs depends on the incident count rate. In this work, we demonstrate that this...

Quantifying Frontier LLM Capabilities for Container Sandbox Escape

Large language models LLMs increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, creating novel security risks. To mitigate these risks, agents are commonly deployed and evaluated in isolated "sandbox" environments, often implemented using...

AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply uniform detection strategies, which may not account for...

Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence

The Forensics Investigations Network in Digital Sciences FINDS Research Center of Excellence CoE, funded by the U.S. Army Research Laboratory, advances Digital Forensic Engineering Education DFEE through an integrated research education framework for AI enabled cybersecurity workforce development...

Threat modeling AI applications

Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...

Cryptographic Fragility of Standard Quantum Repeater Protocols

The security of the proposed quantum Internet relies on repeater protocols designed under the assumption of stochastic, characterizable noise. We demonstrate that in adversarial environments this assumption induces performance vulnerabilities for computationally bounded repeater nodes. We show th...

ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution

Intrusion detection in IoT and industrial networks requires models that can detect rare attacks at low false-positive rates while remaining reliable under evolving traffic and limited labels. Existing IDS solutions often report strong in-distribution accuracy, but they may degrade when evaluated ...

Lifecycle-Integrated Security for AI-Cloud Convergence in Cyber-Physical Infrastructure

The convergence of Artificial Intelligence AI inference pipelines with cloud infrastructure creates a dual attack surface where cloud security standards and AI governance frameworks intersect without unified enforcement mechanisms. AI governance, cloud security, and industrial control system...

AdapTools: Adaptive Tool-Based Indirect Prompt Injection Attacks on Agentic LLMs

The integration of external data services e.g., Model Context Protocol, MCP has made large language model-based agents increasingly powerful for complex task execution. However, this advancement introduces critical security vulnerabilities, particularly indirect prompt injection IPI attacks...

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions

The evaluation of Large Language Models LLMs for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an automated agen...

From Exposure to Exploitation: How AI Collapses Your Response Window

We’ve all seen this before: a developer deploys a new cloud workload and grants overly broad permissions just to keep the sprint moving. An engineer generates a "temporary" API key for testing and forgets to revoke it. In the past, these were minor operational risks, debts you’d eventually pay do...

Trojan Horses in Recruiting: A Red-Teaming Case Study on Indirect Prompt Injection in Standard Vs. Reasoning Models

As Large Language Models LLMs are increasingly integrated into automated decision-making pipelines, specifically within Human Resources HR, the security implications of Indirect Prompt Injection IPI become critical. While a prevailing hypothesis posits that "Reasoning" or "Chain-of-Thought" Model...

From Tool Orchestration to Code Execution: A Study of MCP Design Choices

Model Context Protocols MCPs provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments. As MCP-based systems scale to incorporate larger tool catalogs and multiple concurrently connected MCP servers, traditional tool-by-too...

Can Adversarial Code Comments Fool AI Security Reviewers -- Large-Scale Empirical Study of Comment-Based Attacks and Defenses against LLM Code Analysis

AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can degrade large language model LLM performance in code generation. We test whether similar comment-based manipulation misleads LLMs during...

The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

Deep Reinforcement Learning DRL has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, transitioning DRL from laboratory simulations to bespoke cyber environments can introduce numerous issues. This is furthe...

Evasion of IoT Malware Detection Via Dummy Code Injection

The Internet of Things IoT has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has...

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

With the rapid advancement and adoption of Audio Large Language Models ALLMs, voice agents are now being deployed in high-stakes domains such as banking, customer service, and IT support. However, their vulnerabilities to adversarial misuse still remain unexplored. While prior work has examined...

TrapSuffix: Proactive Defense against Adversarial Suffixes in Jailbreaking

Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs. Since suffixes are free-form text, they admit endlessly many surface forms, making jailbreak mitigation difficult. Most existing defenses depend on passive detecti...