PIDS-Bench is presented, a frozen multi-axis benchmark that jointly evaluates attack detection and benign false-positive behavior at fixed thresholds, spanning in-distribution inputs, hard-benign prompts that mimic injection structure without malicious intent, obfuscated attacks, and domain and structural distribution...
Yusuf Khalid Shire, Sang-Chul Kim· IEEE Access· 1 citation
Results show that ActGuard reduces attack success rates to a level comparable to state-of-the-art defenses while maintaining task utility close to the no-attack setting, achieving a favorable security-utility trade-off.
Bing-Zheng Wang, Xiao-Yan Gu, Wen-Tao Wang et al.· 1 citation
A structural defense is presented: remove tenant identity from the Model Context Protocol (MCP) tool schema, bind scope to a verified credential, and enforce it below the agent, resulting in a tenant-isolation argument that depends on enforceable interfaces and credentials rather than model compliance.
Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Asher Ali et al.· 0 citations
This work presents TriCalRAG, a benchmark evaluating open-weight LLMs served locally via vLLM on a single high-memory workstation GPU against a classical LSTM-based log anomaly detector (DeepLog), across four real, publicly available log datasets (BGL, HDFS, Thun-derbird, OpenStack).
Rohit Patel, S. K. Mohanty, Jeenal Chaudhary· 0 citations
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SENTINEL is presented, a multi-pathway architecture integrating BERT-based semantic encoding, character-level CNN for obfuscation invariance, inter-command attention for multi-stage pattern recognition, and autoencoder-based anomaly scoring, which confirms structural architectural value beyond data-driven robustness al...
Ahad Bin Islam Shoeb, Kamrul Hasan, Jamal Uddin Tanvin et al.· 0 citations
AGENTQ is proposed, an attack framework that combines layer-banded LoRA injection with partial-PGD repair over a multi-codebook quantization-equivalence class that preserves normal agentic capability while concentrating malicious behavior in the quantized model, underscoring the need to make quantization-aware safety e...
Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provide personalized assistance. This access creates a persistent privacy risk: the agent must decide whether a given sensitive information should be disclosed to a particular p...
A targeted prompt-level defense is evaluated and finds that it can reduce memory injection in many settings, but provides limited protection once the persistent memory has been poisoned.
Shu-Huai Huang, Jing-Feng Zhang, Hong Jia· 3 citations
Results indicate that LLMs provide a practical approach for automated vulnerability identification in JavaScript code, particularly when combined with task-aligned supervision, though they should complement rather than replace existing security analysis workflows due to limited recall and uneven performance across vuln...
Manit Kaushik, Ishir Bhardwaj, Pranav Gupta et al.· 0 citations
TyPatch is presented, which decouples patch-specific defect semantics from analyzer implementation and uses 88.3-90.1% fewer generation tokens than the state-of-the-art complete-checker construction workflow, while its initial report pools achieve 3.95 times the precision of those produced by that workflow.
Evaluating XGBoost and RoBERTa-LoRA on two independently collected NetFlow v2 networks across three axes (same-dataset performance, cross-dataset transfer, and adversarial evasion) reveals no universal winner, arguing for evaluating NIDS models along multiple independent robustness axes, and with more than one metric p...
A protocol-aware empirical audit is introduced in which the server commits to a single shared candidate bank and replaces roughly 1% of its entries with probes derived from a known, non-private canary, to quantify the gap between formal worst-case privacy and leakage achievable through protocol-valid candidate-bank man...
Sai Aparna Aketi, Enayat Ullah, Shripad Gade· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
