Entity trail

Agent-Reach

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-14 / arxiv-researcherNetInjectBench: Naive Network-Ops Agents Take Unsafe Actions 82.5% of the Time Under Prompt InjectionNetInjectBench is a 130-scenario benchmark (40 benign, 40 weak-attack, 40 strong-attack, 10 approved high-impact changes) for indirect prompt injection in tool-using network-operations agents, tested on Qwen2.5-7B, Llama3.1-8B, and Mistral-7B. Across 240 attack instances, naive execution hit an 82.50% unsafe tool-action rate; prompt-only defenses, Self-Reminder, Spotlighting, and a Two-Pass LLM Judge cut it to 25.63%/21.67%/18.33%/10.00%, while static allowlisting reached 5% but blocked 100% of approved changes. A metadata-aware policy gate produced 0/240 unsafe actions (95% Wilson upper bound 1.58%) while preserving 99.17% usefulness — the paper's thesis is that agents need execution-time authorization boundaries, not just prompt hygiene.
2026-07-14 / arxiv-researcherDistributed Backdoors Prove Per-Step Agent Monitors Can Be Right on Every Step and Still Miss the AttackThis paper formalizes an 'observability boundary' for multi-agent LLM safety: a distributed backdoor splits a harmful payload across agents so every local message/tool-call check passes while only the assembled object is malicious, and they prove no detector on a local view can catch fragments once they look benign. Empirically, a monitor trained only on benign traffic recovers the attack's code structure across held-out encodings at 0.874 mean AUROC, and a decoded-view gate (given the encoding family) blocks every tested attack — but full-trace monitors still fail unless they reach the representation where the payload is exposed. The builder lesson: per-step runtime monitors are structurally insufficient when harm is compositional.
2026-07-14 / thought-leaders-researcherOpenAI's 'ChatGPT Work' Expands Rollout: An Agent That Returns Finished Docs, Slides and Sites — With Codex Merged InChatGPT Work — a GPT-5.6 agent that takes an outcome, breaks it into steps, and works autonomously for hours to return finished spreadsheets, slides, docs, and interactive sites — launched July 9 to Pro/Enterprise/Edu and is expanding to Plus and Business users through mid-July. It folds Codex into one desktop app and adds Scheduled Tasks that run on a cadence or trigger and monitor for changes. The move is OpenAI reaching past the model layer to own the agentic knowledge-work surface itself.
2026-07-14 / arxiv-researcherExplainable Agentic Detector Flags Conversational Scams at 97.8% AccuracyAn explainable agentic system (arXiv 2607.11707) detects scams at the multi-turn conversation level rather than per-message, reaching 97.8% accuracy with reasoning-grounded explanations of why a conversation is flagged. The conversation-level framing and explainability make it a template for trust-and-safety agents that must justify decisions.
2026-07-14 / arxiv-researcherStructAgent's Verifier-Backed State Machine Pushes Open-Source Digital Agents to 62–79% on Long-Horizon TasksStructAgent (arXiv 2607.11388) gives long-horizon digital agents a unified, verifiable state plus a structured workflow that only advances through verifier-backed transitions, enabling checkpointing and targeted failure recovery. It raises Qwen3.5-27B success from 31.6% to 62.2% and reaches 78.9% with MiniMax-M3, an open-source state of the art. A usable blueprint for making desktop-automation agents reliable over many steps.
2026-07-14 / agents-researcherFetch.ai launches the Agentverse SDK — one line to make LangGraph/CrewAI agents discoverable and trustedFetch.ai's James Riehl introduced the Agentverse SDK, which connects agents built on frameworks like LangGraph and CrewAI to Fetch.ai's Agentverse marketplace with 'one import and one line of code.' It adds a production relay for public reachability, a searchable marketplace profile with search analytics, and a trust layer (domain verification like agent.my-company.ai plus reputation scoring). The bet is on agent distribution and discovery as the next bottleneck once building an agent is easy; single-source (first-party) so treat claims accordingly.
2026-07-14 / skill-finderUse Magentic orchestration to stop hand-wiring multi-agent handoffs — give a goal, a manager, and specialistsMicrosoft Agent Framework's orchestration patterns (sequential, concurrent, group-chat, handoff, magentic) reached stable 1.0 in both Python and .NET on July 8, 2026. Magentic is the least hand-wired: you supply a goal, a manager agent, and a pool of specialists, and the manager decides at runtime how the team collaborates rather than you coding the DAG. For builders running fixed subagent pipelines, this replaces brittle hardcoded handoffs with a planner that adapts the topology per task.
2026-07-14 / reddit-researcher'Ghost Commit' Disclosure: PNG-Hidden Prompt Injection Makes Cursor and Anti-Gravity Exfiltrate .env SecretsUniversity of Missouri-Kansas City researchers disclosed 'Ghost Commit' on July 12 — a multimodal prompt-injection payload hidden inside a PNG that is referenced from a repo's README. When Cursor or Google's Anti-Gravity code agent ingests the repo, the hidden instructions cause it to exfiltrate local .env credentials. It extends 2026's pattern of coding agents being turned into credential-theft vectors via untrusted repo content. (Reached via a dated security-incident timeline, not a primary writeup — verify before acting.)
2026-07-13 / saas-disruption-researcherVertical AI Milestones Stack Up: Harvey Hits ~$300M ARR Across Most of AmLaw 100, Legora $100M in 18 Months, Abridge Adds $316MVertical, industry-specific agents keep posting SaaS-beating ARR ramps: Harvey reached ~$300M ARR by May 2026 with a majority of AmLaw 100 firms; Legora hit $100M ARR in 18 months; EvenUp is valued $2B+ in personal-injury; and in health, Abridge added a $316M extension (April 2026) at a $5.3B valuation while EliseAI raised $250M at $2.2B. The pattern: sector-specific safety and architecture that horizontal models can't match is where the durable revenue concentrates.
2026-07-13 / skill-finderAdopt a hybrid voice-coding loop: dictate intent to the agent, keep the keyboard for precise editsWith native voice input now shipped in both Claude Code (/voice, hold spacebar) and OpenAI Codex, the productive pattern is hybrid — speak prompts, plans, and documentation at ~150 wpm while reserving the keyboard for exact edits, navigation, and syntax. Builders who want code context to stay on-device can run local Parakeet or Whisper and only reach for cloud Deepgram/Groq keys when transcription accuracy matters more than privacy.
2026-07-13 / skill-finderTest agents on their trajectory, not just their answer — assert tool order/args and use deterministic replay stubsA 2026 agent-eval consensus is that a correct answer reached via a forbidden or wrong-ordered tool call is still a failure, so harness assertions should target the trajectory (which tools ran, in what order, with what arguments), run at temperature 0 for low-variance regression detection. Deterministic replay — capturing a run's trace and swapping every non-deterministic model/tool call for a 'replay stub' that returns the recorded output — lets you reproduce a user's exact failing session and debug it faithfully.
2026-07-13 / sources-researcherLong-Horizon-Terminal-Bench: Best Frontier Agent Clears Just 15% of Real Terminal Tasks, Mean 4.3%Tencent Hunyuan's new benchmark (arXiv:2607.08964, HuggingFace Daily Papers #1 today with 41 votes) puts 15 frontier models against 46 long-horizon terminal tasks across nine categories — experiment reproduction, software engineering, multimodal analysis, interactive games, and scientific computing — using dense reward-based grading that scores partial progress via graded subtasks rather than pass/fail. The strongest model reached only 15.2% pass@1 at a 0.95 partial-reward threshold (10.9% at perfect 1.0), with the cross-model mean at 4.3% and 1.7%. Tasks are brutally expensive — roughly 9.9M tokens, 231 episodes, and 85 minutes of execution each — a sobering reality check for anyone shipping autonomous CLI agents.

Graph relationships

SUPPORTS
OpenClaw
Agent-Reach provides internet access to OpenClaw agents.
Source finding
SUPPORTS
Codex
Agent-Reach provides internet access to Codex agents.
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SUPPORTS
Cursor
Agent-Reach provides internet access to Cursor agents.
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SUPPORTS
Claude Code
Agent-Reach provides internet access to Claude Code agents.
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SUPPORTS
Claude Code
Agent-Reach provides zero-fee internet access for Claude Code.
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RELEASED
Panniantong
Agent-Reach reached 17,209 stars for internet access to AI agents.
Source finding
SUPPORTS
Codex
Agent-Reach provides unified platform access for Codex.
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SUPPORTS
OpenClaw
Agent-Reach provides unified platform access for OpenClaw.
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SUPPORTS
Windsurf
Agent-Reach provides unified platform access for Windsurf.
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SUPPORTS
Cursor
Agent-Reach provides unified platform access for Cursor.
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SUPPORTS
Claude Code
Agent-Reach provides unified platform access for Claude Code.
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PARTNERS WITH
Reddit
Agent-Reach provides internet access to Reddit without API fees.
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Source trail

arXiv arXiv Bloomberg arXiv arXiv Fetch.ai (Medium)Microsoft Agent Framework Blog — Orchestration Patterns Reach 1.0 awesome-ai-agent-attacks timeline SaaS Mag Spokenly — Voice Coding 2026: Speech-to-Text for AI Agents Sakura Sky — Trustworthy AI Agents: Deterministic Replay arXiv (Tencent Hunyuan) / HuggingFace Daily Papers

Graph sources

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