Know the Normal, Track the Attack: Context-Grounded and Stateful LLM Investigation over System Provenance

Provenance-based intrusion detection systems PIDSs identify suspicious activity in audit streams, but their outputs remain difficult to turn into coherent attack narratives. Direct LLM analyses of local anomalous subgraphs lack deployment-specific normal-behavior knowledge and validated attack...

From Sharp Eyes to Expert Mind: Internalizing Expert Knowledge in MLLMs for Tampered Text Detection

Tampered Text Detection TTD is essential for safeguarding document authenticity in security-critical workflows. Existing expert models are effective at capturing subtle manipulation traces but often generalize poorly across diverse document domains, while Multimodal Large Language Models MLLMs...

Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

Machine learning ML and deep learning DL have dominated Intrusion Detection System IDS research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection an...

Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure

Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetr...

Verifying Graceful Degradation in a Distributed Malware-Detection System with SPIN

Modern endpoint malware detection is distributed: a lightweight agent on each endpoint collects features from a scanned file or process, sends them to a remote server for analysis, and then enforces the returned verdict locally by blocking, quarantining, or disinfecting. Because the endpoint acts...

Malicious code in aseity (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector d65d0df302f9fdd618d08a8bc095702f5b6594712ba82205c758c710fee9006e setup.py invokes aseity.compat.ensurecompat, which base64+zlib-decodes the DATA constant shipped in aseity/shimdata.py and passes the resulting Pytho...

Where the Numbers Come From: Auditing Evaluation in Provenance-Based Intrusion Detection

Reproducing a provenance-based intrusion detector's score does not establish what that score says about its emitted alarms or the information its encoder uses. We audit nine released implementations, execute four detectors using their own code, and isolate three measurement effects. First, a...

Estimation Is Not Enough: Carpet-Bombing Detection Via Per-Packet Uniformity Testing

Carpet-bombing attacks spread traffic uniformly across one or more destination IP prefixes, keeping every host in the prefix below alarm thresholds while exhausting prefix-level defenses. Existing carpet-bombing detectors run at seconds-to-minutes latency, too slow to respond within the attack...

Specter NFC Reader / Skimmer Bug-Sweep 3.1.1

Specter turns your Flipper Zero into a pocket counter-surveillance bug-sweep for active 13.56 MHz NFC readers - a hidden card skimmer slipped into a payment terminal, a covert reader behind a door panel, a rogue logger taped under a desk. It passively senses the RF carrier that any powered-on...

Agentic Commerce Bench: Measuring Fraud Detection for Agents That Spend Money

AI agents now hold spend authority and settle payments without per-action human confirmation. The resulting loss is often not a security failure: a counterparty with the correct domain, the correct settlement address and a genuinely delivered service can charge more than it should, and no check...

modbus-scanner

Modbus Detection Scanner A multithreaded C scanner for detecting Modbus/TCP services using libmodbus. The scanner does not rely only on TCP port 502 being open. Instead, it establishes a Modbus/TCP connection and sends an application layer Modbus request. A valid Modbus response is used as the...

Context-Aware Functional Modeling for Android Third-Party Library Detection

Third-party libraries TPLs are widely used in Android apps, but their reuse can introduce security risks and interfere with downstream program analyses. Existing Android TPL detection approaches face two key limitations: their hand-crafted features are fragile under aggressive code transformation...

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

Large language models LLMs have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic...

Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment between Antivirus Software and Learning-Based Detectors

Machine-learning ML-based malware detectors are commonly trained using labels obtained from antivirus AV engines and aggregation services e.g., VirusTotal. This practice assumes AV-generated labels provide reliable supervision. However, small byte-level modifications can substantially alter AV...

FARE: Forensic Acceptance Region Estimation for Catching Bait-And-Switch Image Generators

Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates a practical challenge: a provider may pass governance certification with one generator and later silently switch to a...

A Three-Party W-State Quantum Secret Sharing Protocol with X-Gate Encoding and Forbidden-Outcome Detection

We develop a new three-party quantum secret-sharing QSS protocol based on a three-qubit W state. This protocol encodes the secret-sequence bits using X gates and employs randomly selected Hadamard operations and measurement bases to generate information and security-test rounds. We evaluate the...

Improving the Reliability of Anomaly Detection for Encrypted OPC UA Traffic over Private 5G

Open Platform Communications Unified Architecture OPC UA is increasingly deployed over private 5G networks in industrial environments, where end-to-end encryption prevents payload inspection by network-based intrusion detection systems IDSs. Although payload-agnostic statistical features extracte...

DistillGuard: Malicious NPM Package Detection and API Attack Chain Analysis Via Static Graph and LLM Distillation

The Node.js ecosystem heavily relies on NPM packages, and software supply chain attacks targeting malicious NPM packages are rampant. Malicious code primarily triggers during package installation, import, and runtime. Traditional static analysis fails to understand code semantics; machine...

Prompt Injection Detection for Email Agents through Attack Chain Modeling

Large language model email assistants are particularly vulnerable to indirect prompt injection because untrusted email content can be retrieved into the model context and influence subsequent tool use. Existing prompt injection detectors mainly formulate this problem as binary malicious text...

CVE-2026-63451

A flaw was found in Suricata, a network Intrusion Detection System. A local user can craft a detection rule that combines frame inspection without content and a transformed match without content. Loading this specially crafted rule can trigger a heap buffer overflow, leading to a denial of servic...