Search: “nmap”

5 stories in the last 30d

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Research 3 AI tools & infra 1 Ransomware 1

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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.CRSep 14, 13:13 UTC · 2d agoResearch

numbat - AI agent observability, (Fri, Sep 4th)

SANS reviews Perplexity's open source numbat, a Go-based tool giving security teams observability, detection rules, and enforcement for AI agents like Claude and Gemini.

Numbat, Perplexity AI's open source observability tool, monitors desktop, CLI, IDE, and gateway AI agents through local hooks, OTLP/HTTP logs, and on-disk session artifacts. It ships detection rules mapped to MITRE ATT&CK (e.g., recon.network_sweep / T1046), supports enforcement mode, and packages investigations with SHA256-verified manifests and timelines. The SANS review positions it as a response to unmanaged AI agent and MCP server sprawl highlighted by the OpenAI/Hugging Face incident.

SANS Internet Storm CenterSep 5, 03:39 UTC · 11d agoAI tools & infra

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security LabsSep 11, 00:00 UTC · 5d agoResearch

Aurora Ransomware Operators Use Cursor AI in Attacks Against 10 Targets

Aurora ransomware operators used Cursor AI running Claude Sonnet to plan and execute intrusions against dozens of organizations in nine countries.

CloudSEK and Gambit Security analyzed exposed infrastructure tied to the Russian-speaking Aurora (Aur0ra) group, revealing months of activity against more than 20 organizations across nine countries between April and July 2026, with 33 victims listed by Ransomware.Live. The operator used the Cursor agentic coding assistant to plan attacks in Russian, including an AD CS exploitation plan, and Gambit observed Cursor Agent (running Anthropic's Claude Sonnet) performing hands-on exploitation tasks such as Nmap scanning, NetExec enumeration, NTLM relay, and certificate attacks against 10 targets. Attacks begin with email bombing plus IT help desk vishing via Xray-core, followed by SMB/LDAP/WinRM/RDP lateral movement, log clearing, Defender disabling, and exfiltration; the Windows and Linux/ESXi encryptors are built from a single Zig codebase, with shadow copy deletion and VM-killing before encryption.

The Hacker NewsAug 31, 11:47 UTC · 16d agoRansomware in the wild1

The AI Malware Maturity Gap

Recorded Future introduces AIM3, a five-level maturity model for AI malware, showing current attacker AI use is mostly AI-assisted rather than autonomous.

Recorded Future proposes AIM3, a five-level model defining AI malware from LLM-translated to LLM-embedded, spanning experimentation to fully autonomous agentic campaigns. Public examples remain early-stage: PROMPTFLUX uses Google Gemini to rewrite its VBScript dropper (Level 1), while Lamehug/PROMPTSTEAL, attributed to APT28, invokes the HuggingFace API to generate reconnaissance commands (Level 3). The authors argue most current AI malware augments existing tradecraft rather than enabling one-click autonomous attacks.

Recorded FutureAug 26, 00:00 UTC · 21d agoResearch