Search: “SoftPerfect Network Scanner”

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Ransom Busters Claims It Hacked Ransomware Servers, Asks Victims for Up to $60,000

GuidePoint reports a ransomware affiliate posing as 'Ransom Busters' charges victims $20,000-$60,000 to delete stolen data, and details UNC6671's $8M AitM extortion wave.

GuidePoint's GRIT team reports that 'Ransom Busters', likely a ransomware affiliate active across multiple RaaS operations including DragonForce, Settra and Anubis, proactively emails victims claiming it deleted their stolen data and backups for a $20,000-$60,000 fee, citing claimed access to RaaS administrative panels for over three years. Two analyzed intrusions shared tooling: SoftPerfect Network Scanner for reconnaissance, s5cmd-based exfiltration to AWS cloud storage, an RMM tool installed via PowerShell, a backdoor account with password 'Numlock!123' and the same attacker hostname DESKTOP-BBETH6K. Separately, GRIT detailed UNC6671's (Cordial Spider) adversary-in-the-middle vishing operation running since April under five extortion brands, with more than $8 million across 15 Bitcoin wallets, an average of $600,000 per payment, and 78 phishing sub-domains across 76 organizations, 40% in financial services.

The Hacker News·Aug 18, 17:35 UTC · 28d agoThreat actor in the wild1

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Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

ExCYDER framework self-verifies anomaly detection alerts for DER power grids using LightGBM and SHAP, reaching over 98% detection accuracy.

The paper presents ExCYDER, an explainable AI anomaly detection framework for Distributed Energy Resource networks that combines LightGBM with SHAP to validate whether each model decision aligns with its feature-attribution evidence. On a realistic DNP3 dataset it achieved over 98% detection accuracy, 14.5 ms SHAP latency per alert, and confidence deviation within 5%. The self-verifying mechanism distinguishes coherent from inconsistent alerts, improving interpretability and auditability for DER-focused security operations centers.

arXiv cs.CR·Sep 11, 00:19 UTC · 5d agoResearch

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Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams

An event-native spike encoding framework lets recurrent spiking neural networks run intrusion detection directly on heterogeneous packet and CAN bus streams.

The paper maps heterogeneous cyber events—categorical identifiers, local frequency context, and inter-event timing—directly into sparse spike-compatible inputs for spiking neural networks. This avoids flow aggregation and fixed windows that add buffering latency and obscure temporal structure in traditional IDS pipelines. Compact recurrent SNNs under edge-oriented neuromorphic hardware constraints achieve a hybrid anomaly metric of 0.987 on packet-level Network IDS and 0.980 on message-level CAN IDS.

arXiv cs.CR·Sep 14, 15:55 UTC · 1d agoResearch

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Honeypot-Omaha and batch.py [Guest Diary], (Wed, Sep 2nd)

A SANS ISC guest diary describes batch.py, a Python tool that consolidates honeypot logs and enriches IOCs with threat intelligence data.

Written by a SANS.edu BACS intern, the diary explains analysis of the DShield Honeypot-Omaha sensor, which uses Cowrie to emulate SSH and Telnet and log attacker activity. The author's batch.py script implements a four-phase pipeline with SHA-256-generated master and guest authentication to consolidate JSON and log files, correlate data via external APIs, and produce MITRE, CVE, geolocation, threat-score and fingerprint enrichment for investigated indicators.

SANS Internet Storm Center·Sep 3, 02:02 UTC · 13d agoTools1

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Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling

Study shows nested-window Bi-LSTM architectures do not improve faster-than-Nyquist detection; pre-whitening plus BCJR distillation cuts bit error rates.

Across roughly 260 controlled trainings, processing nested intersymbol-interference windows in separate recurrent branches never significantly beat a plain Bi-LSTM at matched parameter budgets. The authors attribute the limitation to the observation model rather than architecture, and instead pre-whiten inputs and distill BCJR soft posteriors into the network. With 3.4% more parameters, the method reaches 1.05x the BCJR bit error rate at compression factor 0.8 and 1.89x at 0.7, improving to 1.47x with a wider whitened window.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:07 UTC · 8d agoAI research

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Why Is SHAP Not a Reliable Standalone Explanation Framework for Malware Detection?

arXiv paper shows SHAP gives unreliable standalone explanations for malware detection, with attribution dilution and sign reversal in dependent PE feature spaces.

The paper argues SHAP's formal guarantees are insufficient for reliable malware interpretation because the explained feature-coalition game is fixed only by analyst choices, not by malware behavior in the data. In static Portable Executable feature spaces, dependent feature groups cause conditional SHAP to dilute credit by a factor of 1/m across redundant features, attribute importance to features the model never uses, and even reverse attribution signs; interventional SHAP queries off-manifold coalitions no real executable exhibits. Experiments on EMBER-2018, EMBER-2024, and BODMAS with fixed LightGBM and XGBoost detectors confirm these effects. The authors position SHAP as a limited diagnostic requiring explicit data-distribution statements and domain validation, not a standalone explanation framework.

arXiv cs.CR·Sep 4, 02:00 UTC · 12d agoResearch1

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Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

A unified ViT compression pipeline (H-BAC pruning, quantization, distillation) cuts plant-disease models 54.5x to 6.01 MB while keeping 95.13% accuracy.

Researchers combined Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation to compress Vision Transformers for on-device chilli plant disease detection in India. On a 3-class cross-village, cross-device out-of-distribution dataset, the integrated pipeline reduced model size from 327.42 MB to 6.01 MB (54.5x) at 95.13 +/- 2.32% accuracy, matching the 95.13% FP32 baseline. Ablations also show a directly trained 6.01 MB INT8 student reaches 94.87% accuracy, indicating where pruning and distillation add limited value.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:40 UTC · 11d agoAI research

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Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning

Researchers show malicious federated learning clients can probe broadcast classifiers to recover deleted samples, exposing exact label leakage on MNIST and CIFAR-10.

The paper shows that federated unlearning systems broadcasting updated linear classifiers leak compact additive training summaries to clients. A malicious client can submit known changes, identify server states from returned classifiers, and compare states around an isolated deletion to expose the deleted sample, class, or client summary, potentially enabling reinsertion. On MNIST and CIFAR-10, high-precision broadcasts allowed exact label recovery for every tested deletion, while lower precision sharply reduced fine-grained recovery.

arXiv cs.CR·Sep 3, 20:59 UTC · 12d agoResearch

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Identifying Agentic Automation with Behavioral Telemetry

Akamai describes detecting autonomous AI browser agents like Comet using Masked Autoencoder Transformer models on sparse behavioral telemetry.

Akamai researchers present a behavioral telemetry approach for identifying agentic automation in web traffic. Masked Autoencoder Transformer models are used to detect the sparse behavioral signals produced by autonomous AI browser agents such as Comet. The work targets traffic classification and bot detection rather than a specific vulnerability, and becomes more relevant as agentic browsing adoption grows.

Akamai Blog·Aug 19, 19:00 UTC · 27d agoAI safety & security1

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PEEP Turns Chrome and Edge Into Post-Compromise Backdoors for Host Command Execution

SOCRadar disclosed PEEP, a post-exploitation Chromium extension toolkit that turns Chrome and Edge into host-level backdoors via native messaging and C2 polling.

SOCRadar researchers detailed PEEP, a Chromium-based post-exploitation toolkit masquerading as a 'Smart Bookmarks' extension injected directly into Chrome and Edge profiles by forging Secure Preferences integrity values, bypassing Web Store checks. A native-messaging host binary (nm_host.exe) extends the browser agent to host-level command execution, file management, credential theft, and session hijacking, while the extension polls C2 endpoints at 206.237.30.232 or xfjcc.fun every 30 seconds over plaintext HTTP and exfiltrates browsing history, cookies, and tab metadata. PEEP is derived from the open-source RedExt red teaming framework, adds PowerShell persistence scripts, and includes a Linux-targeting Python script, suggesting cross-platform intent. The activity is unattributed but contains Chinese-language artifacts; a C2 /health endpoint showed 34 agent entries and 10 active sessions.

The Hacker News·Sep 7, 18:12 UTC · 8d agoMalware in the wild

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CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:26 UTC · 8d agoAI research1

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The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 15d agoAI safety & security1

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SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Codenew

SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.

SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.

arXiv cs.CR·Sep 15, 14:35 UTC · 13h agoResearch

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Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Researchers propose DriftXpert, a concept-drift-adaptive network intrusion detection system validated on enterprise networks, addressing degraded AI-based NIDS performance in dynamic traffic.

AI-based network intrusion detection systems assume static data distributions and degrade under concept drift, raising false positives in dynamic environments. DriftXpert uses a two-stage offline framework: an unsupervised latent-manifold anomaly metric to detect traffic drift, and representation consistency alignment with cross-epoch neuron weight aggregation and selective freezing to transfer knowledge without catastrophic forgetting. Experiments on public datasets and a real enterprise network show effective adaptation to drifted data while retaining known-attack detection.

arXiv cs.CR·Sep 8, 11:58 UTC · 7d agoResearch

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TasmScan: Continuation-Aware Taint Analysis for TVM Bytecode with Savelist Abstractionnew

TasmScan introduces source-free taint analysis for TON smart-contract bytecode, detecting 95.3% of defects with 96.8% precision and 17x speedup.

TasmScan is the first bytecode-level static analysis framework for the TON Virtual Machine, enabling cross-continuation data flow reasoning without source code by modeling savelist semantics through forward register analysis with formal over-approximation guarantees. It lifts bytecode into a typed intermediate representation (TASIR) and performs path-sensitive taint analysis. On a 208-contract benchmark with human-confirmed ground truth it detects 95.3% of defects across five classes at 96.8% precision, and resolves 294,546 dynamic continuation targets with 100% precision across 2,921 registry contracts. It achieves a 17x median speedup over symbolic-execution baselines.

arXiv cs.CR·Sep 15, 11:01 UTC · 17h agoResearch

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Best Practices for Good Endpoint Hardening | Huntress

Huntress outlines endpoint hardening best practices, citing exposed RDP, RMM tool abuse, and ClickFix social engineering as common SMB intrusion paths.

Huntress published defensive guidance on endpoint hardening for small and mid-sized businesses, drawing on observations from its SOC. The post describes common intrusion vectors: internet-exposed RDP brute-forced by automated scanners, phishing emails delivering attacker-controlled remote monitoring and management (RMM) tools, with Huntress reporting a 277% spike in RMM abuse in 2025, and ClickFix attacks using fake CAPTCHA pages that trick users into running malicious commands. Recommended controls include scanning for exposed RDP, SSH, and VPN interfaces, removing unneeded local admin rights, enabling Windows Defender tamper protection, disabling SMBv1, and standardizing on one approved remote access tool, guided by CIS and NIST frameworks.

Huntress·Sep 10, 15:00 UTC · 5d agoAdvisory

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TPMSpy: Validation of Measured Boot Systems by Low-Level Tracing of TPM Usage

Researchers present TPMSpy, a platform-agnostic method validating TPM Measured Boot via low-level tracing, finding inconsistent Linux systemd measurements that break remote attestation and LUKS decryption.

An arXiv paper (2609.05011) introduces TPMSpy, a method that analyzes virtualized system–TPM interactions to independently reconstruct and validate TPM Event Logs without relying on the quoting mechanism, applicable to open and closed systems and demonstrated on Linux and Windows. A longitudinal analysis of Linux systems running systemd versions 245–258 (2020–2025) found wide divergence in Measured Boot usage, undocumented behavioral changes, and no common usage pattern. The authors report inconsistent measurement of user-space systemd services, which prevents reliable remote attestation and LUKS disk decryption on affected systems.

arXiv cs.CR·Sep 4, 11:23 UTC · 11d agoResearch

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Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting

Semi-automated pipeline converts Trivy, Semgrep, Nmap output into MulVAL attack graphs for agentic pentesting, 53.7% mean vulnerability coverage in CyBench.

The paper presents a semi-automated pipeline that parses Trivy, Semgrep, and Nmap findings into MulVAL predicates and uses an LLM-assisted process to build domain-specific Datalog rules linking scanner evidence to attack techniques. MulVAL/XSB then performs symbolic inference to generate structured, auditable attack paths for agentic pentesting. Evaluated on 54 web CTF tasks from CyBench, every task produced at least one goal-reaching graph with 53.7% mean ground-truth vulnerability coverage, 51.9% full coverage, and an 83.9% noise-path rate. Median end-to-end runtime was 24.9 seconds, making the pipeline runtime-practical for agentic workflows.

arXiv cs.CR·Sep 14, 13:13 UTC · 1d agoResearch

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AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications

AVP-Inspect automated testing finds 58% of 324 Apple Vision Pro apps show privacy violations, with over 60% of network traffic flows undisclosed.

Researchers built AVP-Inspect, a dynamic analysis framework combining custom hardware device control, 3D UI exploration, and a unified privacy taxonomy for Apple Vision Pro. Testing 324 App Store apps for 20 minutes each found 188 (58.0%) with at least one privacy violation. More than 60% of observed network traffic flows were not properly disclosed, extending prior XR privacy work beyond Android-based devices such as Meta Quest.

arXiv cs.CR·Sep 8, 01:24 UTC · 8d agoResearch

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Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

SWEADV benchmark shows adversarial issue descriptions make LLM program-repair agents write insecure fixes in 51.7% of cases, evading most detection tools.

Researchers built SWEADV, a benchmark of 750 adversarial issue descriptions derived from 150 SWE-bench Verified repair tasks, covering command execution, deserialization, path traversal, denial of service, and weak hashing attack types. Tested on mini_swe agents backed by GPT-5-Mini, MiniMax-M2.5, and DeepSeek-R, adversarial descriptions induced malicious behavior with successful repair in 51.7% of cases. Detection was weak: LLM-as-judge pre-repair screening reached only 62.3% accuracy, and post-repair detection via static analysis and LLM-as-judge achieved just 39.4% and 55.4%.

arXiv cs.CR·Sep 14, 17:53 UTC · 1d agoAI safety & security

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A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK

Trace2ATT&CK maps eBPF kernel telemetry to MITRE ATT&CK via provenance graphs and RAG with local open-weights LLMs, validated on 347 Atomic Red Team tests.

Trace2ATT&CK collects kernel-level events via eBPF, correlates attacker commands into a provenance graph, and derives compact graph representations suitable for LLM-based reasoning, mapping behavior to MITRE ATT&CK techniques with ranked candidates and rationales. Mapping uses both pure LLM prompting and retrieval-augmented generation grounded in the ATT&CK knowledge base. It was evaluated on 347 Linux Atomic Red Team tests using locally deployed open-weights LLMs. RAG consistently improved ATT&CK mapping over pure prompting, and provenance graphs substantially outperformed raw telemetry, without compromising data confidentiality.

arXiv cs.CR·Sep 11, 13:35 UTC · 4d agoResearch

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Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics

Researchers show a single ROS 2 environment variable lets attackers inject fake telemetry and hijack robots while spoofing downstream remote attestation.

A pre-built hook loaded via one modified environment variable covertly intercepts and injects both telemetry and control signals before publication in ROS 2, breaking the physical-to-digital trust boundary in multi-robot task handovers. Attackers can also distribute compromised third-party Docker containers and auxiliary tools embedding the hooks. On a physical Franka Emika arm running Secure ROS 2, the attack injects fabricated telemetry in real time with roughly 3 ms jitter and achieved an 87% success rate even against an AI-based detector. Findings were responsibly disclosed to the ROS 2 development team.

arXiv cs.CR·Sep 8, 05:50 UTC · 7d agoVulnerability1

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Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 5d agoAI research1

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Graph Machine: Towards Better Pretraining via Edges

Researchers propose Graph Machine, an O(n)-state sparse architecture that replaces 75% of Qwen3-0.6B dense layers with only slight loss change.

The paper introduces the Graph Machine (GM), an architecture that maintains an O(n)-sized state accessed through sparse, dynamic routing via pointer-like edges updated differentiably by a referral mechanism resembling pointer chasing. The authors replaced 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrained from scratch on 15.7B tokens. Retrieving 2 of 4,096 tokens per KV head in each sparse layer degrades loss only slightly, while retrieving 4 marginally improves loss over the dense baseline.

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

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Sound Debloating of Redundant Checks in Zero-Knowledge Machine-Learning Circuits

Automated framework soundly removes up to 48.7% of redundant constraints in ezkl and zkml ZK-ML circuits, cutting prover time by up to 72.8%.

The framework uses whole-circuit abstract interpretation and a provenance graph to verify that each removed redundant check (range proofs, sign lookups, bit decompositions) remains entailed by the rest of the circuit, provably preserving soundness. It was evaluated on MLP, CNN, RNN, and transformer circuits generated by ezkl and zkml, with up to 25.3 million constraints. It removes up to 48.7% of constraints and reduces prover time by up to 72.8% without weakening security. Under-constrained circuits in deployed ZK systems have previously enabled attackers to forge transactions and bypass verification.

arXiv cs.CR·Sep 9, 13:27 UTC · 6d agoResearch1

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HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving

HoneyRoute detects malicious LLM serving requests and diverts them to a honeypot model, reaching F1 0.911 with 38 ms median added latency.

HoneyRoute is an inference-serving layer pairing a streaming router (a frozen 0.8B embedding backbone with per-domain MLP heads) with a dual-implementation honeypot and an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus it matches 96% of a two-tier guard-LLM cascade's F1 at 1/385th of its latency with 0% evasion under 13 adversarial transformations. Diverting malicious traffic cuts production token consumption under GCG-suffix flooding by 97.8%, and loop training raises detection F1 to 0.933.

arXiv cs.CR·Sep 8, 06:26 UTC · 7d agoAI safety & security

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Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

Systematic study finds model pruning causes frequency-dependent long-tail forgetting in medical imaging and that gradient-informed methods best preserve explanations.

Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity up to 95%, the study measures predictive performance, explanation stability, and faithfulness. Rare classes degrade earlier and more severely than frequent ones, while explanation reliability depends mainly on the pruning strategy, with gradient-informed methods degrading least. Mechanistic analysis ties explanation collapse to loss of class-discriminative gradients rather than vanishing feature activations, recommending class- and explanation-aware evaluation of compression.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:44 UTC · 8d agoAI research

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To keep the AI hacking genie bottled up, try one-way networks

Intuition Machines CEO proposes data diodes and one-way networks to physically prevent frontier AI models from escaping training sandboxes, citing the OpenAI Hugging Face incident.

Eli-Shaoul Khedouri, CEO of Intuition Machines, argues that sandboxes, permissions, and VMs are insufficient to contain frontier models, pointing to OpenAI's hack of Hugging Face as evidence. He proposes high assurance architectures modeled on classified SCIF environments: one-way optical data diodes for training inputs and telemetry, a sel4-verified receiver, immutable snapshots of registries like PyPI, GitHub, and npm, and mocked web services. He estimates under five percent overhead per gigawatt for such clusters, but notes frontier labs have not adopted them, largely because of competitive speed rather than cost.

The Register · Security·Sep 3, 06:33 UTC · 12d agoAI safety & security

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Dr. Claw: An AI Scientist Workspace for Vibe Research

Researchers release Dr. Claw, an open-source auditable workspace that wraps coding agents like Claude Code for end-to-end AI-assisted research workflows.

Paper 2609.00365 presents Dr. Claw, an open-source workspace that wraps existing coding-agent executors such as Claude Code and Gemini CLI in a controllable, human-in-the-loop research workflow. It uses persistent state objects, a reusable skill library, and multi-executor coordination to make research decisions auditable and recoverable, rather than adding another autonomous agent. Holding the executor fixed, Dr. Claw scores higher on research completeness than a bare command-line agent while preserving an auditable process trail. The code is released under AGPL-3.0 on GitHub (OpenLAIR/dr-claw).

Hugging Face daily papers·Aug 30, 20:00 UTC · 16d agoAI research

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A Feature-Rich Embedded NIDS with eBPF/XDP: Detector and Architecture Trade-offs

eBPF/XDP-based NIDS with Isolation Forest reaches 0.965 live F1 on DDoS replay; gRPC microservices match monolithic accuracy within 2ms overhead.

The paper presents a DDoS-focused network intrusion detection system for transport networks built with Ericsson, combining a statistical baseline with an Isolation Forest trained on flow features from GoFlowMeter, an open-source Go implementation of CICFlowMeter, plus eBPF/XDP kernel-level traffic filtering. On a Raspberry Pi 5 testbed replaying CIC-DDoS2019 as real traffic, the Isolation Forest achieves 0.965 recall/F1 live in the monolithic variant, catching low-volume attack windows the baseline misses. gRPC microservices nearly match monolithic accuracy adding under 2ms per window, while the Kafka pipeline trails by roughly nine percentage points and adds about 27ms.

arXiv cs.CR·Sep 11, 09:00 UTC · 4d agoResearch

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DPRK APTs: Ted backdoor and curlRAT target South Korean media and automotive sectors

Rapid7 uncovered a DPRK-linked Linux toolkit using a HAProxy-embedded ted backdoor, SSH keylogger, and curlRAT against South Korean media and automotive firms.

Rapid7 Labs identified a previously undocumented framework attributed with medium confidence to DPRK actors, targeting South Korean automotive and media organizations likely since early 2025. The toolkit embeds a backdoor compiled into HAProxy 2.8.12 using its filter API, plus trojanized crond, agetty, atd, sshd, and polkitd, an SSH keylogger storing credentials under /var/lib/sshd/, and a curl-based RAT with a watchdog thread. It enables remote command execution, malicious script injection into served webpages (a watering-hole loop), credential harvesting, and long-term surveillance. Hardcoded C2s are associated with APT37 via ThreatFox, and exposed groupware portals and mail servers align with Kimsuky tradecraft; the initial access vector and any CVE remain unconfirmed.

Rapid7 Blog·Sep 4, 12:00 UTC · 11d agoThreat actor in the wild1

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