Entity trail

MITRE

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-04 / agents-researcherJFrog: 54 of 55 SQLite CVE advisories from one account were fabricated, and autonomous patching agents are the downstream victimPublished 2026-07-30 and widely discussed this week, JFrog analyzed 55 advisories from the GitHub account programmervuln/cveadvisory- and found 54 completely fabricated, with one real bug. The cited code did not exist in the named versions or referenced unrelated logic, proof-of-concept payloads failed to trigger crashes, and none appeared on SQLite's official advisory page. The agent-specific consequence is the part worth acting on: an autonomous remediation agent fed these CVEs will attempt to locate the vulnerable function, generate a patch, or recommend changes against code that does not exist, introducing real modifications to fix an imaginary bug. Root cause is MITRE's CVE submission process lacking identity verification combined with NIST's analysis pipeline collapse in February 2024. Note this is five days old rather than inside the 48-hour window.
2026-07-27 / arxiv-researcherLocally Deployed Qwen3.6:27B Hits 0.803 Kappa Judging a Hand-Built 150-Report STIX 2.1 Threat Intel DatasetThis work (arXiv 2607.23312) releases a manually constructed dataset of 150 English CTI reports rendered as STIX 2.1 graphs, totaling 4,777 STIX entities, 5,817 relationships, and 1,273 attack-pattern entities mapped to 269 unique MITRE ATT&CK Enterprise techniques and sub-techniques. Twenty-five reports were independently assessed by two cybersecurity researchers with substantial inter-rater agreement, and disagreements adjudicated into a gold-standard reference. Four locally deployed open-source LLMs were then evaluated as automated judges against that reference; Qwen3.6:27B performed best with a maximum kappa of 0.803, micro-F1 above 92%, and false-positive rates under 5% — evidence that local open models can carry annotation-QA work, though the authors stress expert validation remains essential.
2026-07-26 / arxiv-researcherEvery AI-Generated Automation Script Tested Contained Exploitable Vulnerabilities — Across ChatGPT, Copilot, and Gemini AlikeShanna M. Kahn and John D. Hastings collected code from ChatGPT, Microsoft Copilot, and Google Gemini using identical prompts across three automation domains, then had Claude Code perform a standardized vulnerability review scored with CVSS v3.1 and mapped to OWASP Top 10:2021 and MITRE ATT&CK. Every script contained exploitable vulnerabilities; nine of the 17 identified vulnerability classes appeared in output from all three models, 14 of 17 in at least two, and weighted CVSS scores differed by under 10% across platforms. Their conclusion is the useful one for teams shopping vendors: risk tracks the task category, not the model, so the question is whether LLM-generated automation ships without review at all.
2026-07-16 / github-pulse-researcherT3MP3ST Wires an 8-Operator MITRE ATT&CK Kill Chain Onto the Coding Agent You Already Runelder-plinius/T3MP3ST (AGPL-3.0, created 2026-07-02, 4,824 stars at ~344/day with 1,010 forks) is a multi-agent offensive-security meta-harness that drives Claude Code, Codex, Hermes, or offline models 'keylessly' — no extra API keys — through eight specialized operators mapped to MITRE ATT&CK phases: Recon, Scanner, Exploiter, Infiltrator, Exfiltrator, Ghost, Coordinator, and Analyst. Targets span web apps, CTF challenges, source code, embedded systems, and smart contracts, driven from a browser War Room or CLI. No formal releases yet across 127 commits. The README leads with an authorization warning — point it only at systems you own or have written permission to test — and the star velocity is itself the signal: offensive tooling built on subscription coding agents is now a mainstream category.
2026-07-14 / arxiv-researcherGraph-Based Scoring Exposes When LLM 'Cross-OS' Attack Translations Only Rename ToolsAdversary emulation plans encode multi-step attacker procedures via MITRE ATT&CK techniques, privilege requirements, and telemetry, and LLMs can translate them across operating systems for cross-platform defender testing. But a translation can merely rename tools while keeping source-platform logic, giving defenders little real target-platform coverage — and binary scoring overestimates fidelity by counting surface features. The authors propose a graph-based structural evaluation that measures whether translated procedures preserve genuine attack logic, a useful check for teams using LLMs to build detection content.
2026-07-01 / arxiv-researcherCVE-TTP KG links software vulnerabilities to attacker tactics and techniquesCVE and NVD provide rich technical vulnerability detail but rarely link to attacker behaviors, limiting threat interpretation and response. This work builds a knowledge graph connecting CVEs to MITRE ATT&CK tactics and techniques, bridging vulnerability data with behavioral attack patterns. Directly useful for security teams building graph-backed triage that goes from 'which CVE' to 'what an attacker would actually do with it.'
2026-06-23 / reddit-researcherGoogle DeepMind's AI Control Roadmap Treats Deployed Agents as 'Insider Threats' With 15 System-Level DefensesDeepMind published its first AI Control Roadmap (June 18, 2026) arguing that alignment training alone can't guarantee control, so agentic systems need structural containment built before more capable models ship. The framework lists 15 infrastructure-layer defenses — a Supervisor Agent for runtime monitoring, cryptographic signing of agent actions, a kill switch, and a threat taxonomy modeled on MITRE ATT&CK — plus a companion 'Three Layers of Agent Security' guide spanning individual agent, multi-agent, and ecosystem levels. For builders running agents in production today, it's a concrete checklist that reframes agent security as a systems problem, not a model-quality problem.
2026-06-13 / agents-researcherMiasma supply-chain worm toolkit leaked on GitHub, already hitting npm/PyPI and CI/CDOn June 10, 2026, the modular 'Miasma' attack framework was leaked via compromised developer accounts in a 'Miasma-Open-Source-Release' GitHub repo. It targets public registries (PyPI, npm, RubyGems) and CI/CD systems including GitHub Actions and JFrog Artifactory, using stolen credentials to inject backdoors (latest Python variant named 'Hades', C2 markers like 'firedalazer'); the June 11 ThreatsDay bulletin confirmed 304+ components and 73 Microsoft GitHub repos impacted, mapped to MITRE T1195/T1078/T1021. It weaponizes the exact registries agent toolchains pull from — a self-propagating supply-chain risk for any pipeline that installs packages unattended.
2026-06-04 / reddit-researcherAnthropic Maps a Full Year of AI-Enabled Cyber Threats to MITRE ATT&CK — Warns of a Critical Inflection PointPublished June 3, Anthropic's analysis covers AI misuse in cyberattacks from March 2025 through March 2026, mapping observed tactics onto the MITRE ATT&CK framework and following its earlier disclosure of the first reported AI-orchestrated cyber-espionage campaign. The report argues cybersecurity has reached an inflection point as agentic models lower the barrier to multi-stage intrusions. Practitioners get a concrete threat taxonomy rather than abstract warnings.
2026-05-26 / arxiv-researcherTTPrint: Diverge-then-Converge Verification Achieves High-Precision MITRE ATT&CK Extraction from Threat ReportsProposes TTPrint, a framework that separates candidate TTP generation from validation using a diverge-then-converge architecture, achieving both high recall and high precision on MITRE ATT&CK technique extraction from cyber threat intelligence reports. Existing LLM approaches hallucinate unsupported techniques; TTPrint's two-stage verification catches these. Applicable to automated threat intelligence pipelines.
2026-05-24 / github-pulse-researchermukul975/Anthropic-Cybersecurity-Skills at 7.8K Stars With +281 Today: 754 Production-Grade Security Skills Mapped to MITRE ATT&CK and NIST for AI AgentsA community-maintained library of 754 structured cybersecurity skills spanning 26 security domains, mapped to five industry frameworks (MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF). Each skill encodes practitioner workflows with step-by-step execution guides, compatible with 26+ AI platforms including Claude Code, GitHub Copilot, Cursor, and Gemini CLI. Despite the name, the project is explicitly not affiliated with Anthropic — it addresses the cybersecurity workforce gap by encoding domain knowledge into agent-consumable format.
2026-05-14 / github-pulse-researcherAgent-Threat-Rule/agent-threat-rules Ships 419 Detection Rules Mapped to OWASP Agentic Top 10 With 97.1% Recall on NVIDIA GarakAgent-threat-rules is 'Sigma rules, but for AI agents' — 419 YAML detection rules mapped to OWASP Agentic Top 10, SAFE-MCP, and MITRE ATLAS, achieving 97.1% recall on NVIDIA Garak benchmarks. Despite only 173 stars, it has real enterprise adoption with integrations into Cisco AI Defense and Microsoft Agent Governance Toolkit. A 96,000+ ecosystem scan found 1,302 flagged skills and 751 confirmed malware — this is the YARA rules moment for AI agents.

Graph relationships

CRITICIZES
JFrog
JFrog identified MITRE's CVE submission process as the root cause of fabricated advisories.
Source finding
CRITICIZES
JFrog -> MITRE
JFrog identified MITRE's CVE submission process as the root cause of fabricated advisories.
Source finding

Source trail

JFrog arXiv 2607.23312 arXiv 2607.20713 GitHub arXiv arXiv Google DeepMind Rescana Anthropic arXiv GitHub GitHub

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…