GPT-6 Astra needs leaner prompts and fewer guardrails, OpenAI recommends
OpenAI's Eric Provencher advises developers using GPT-6 Astra to shorten skill descriptions, trim AGENTS.md reading requirements, relax approval rules, and define clear completion goals.
OpenAI's Eric Provencher published guidance on adapting developer setups when switching to GPT-6 Astra, arguing that overly long skill descriptions, blanket reading requirements, and rigid approval rules waste context or make the agent stop too early. Skills are Markdown prompt files whose names and descriptions enter Codex's context, and too many or conflicting skills cause truncation and wrong skill selection. He recommends selective document references in AGENTS.md, explicit permissions for safe operations like local test runs, and defining upfront what "done" means, since Astra may stop earlier than GPT-5.6 Sol even without restrictions.
Ask HN: How do you manage skills files?
A Hacker News thread debates whether agent skill files are worth managing, citing 2–4x output-token reductions on flagship models in one company's testing.
Commenters argue skills are stored prompts that help less-technical users compensate for weak prompting, while one participant reports company testing found skills reduce flagship-model output tokens by roughly 2–4x, a gap growing with newer models. Others note skills can bundle reusable scripts and inline commands for deterministic context building, and that harnesses now execute backticked commands before the agent sees the skill. Some argue improving model capability makes downloadable skills redundant.
TrickBot Campaign Uses Fake Payroll Emails to Conduct Phishing Attacks
TrickBot phishing campaign used SendGrid, fake Google Docs, and Drive-hosted downloaders disguised as Word files to deliver a credential-stealing payload.
Unit 42 identified a TrickBot distribution campaign on November 7-8, 2019, using payroll and annual bonus-themed emails sent from likely compromised .edu addresses through the legitimate SendGrid email delivery service. Emails contained links to Google Docs documents linking to downloader executables hosted on Google Drive, further masked behind SendGrid click-tracking URLs. The downloaders, signed by PERISMOUNT LIMITED and displayed with Microsoft Word icons, show a decoy pop-up and retrieve TrickBot payloads from compromised legitimate domains such as savute[.]in and lindaspryinteriordesign[.]com. The newer TrickBot variant stores its files and configuration under %APPDATA%\cashcore.
Mole Ransomware: How One Malicious Spam Campaign Quickly Increased Complexity and Changed Tactics
Unit 42 tracks the new Mole ransomware (CryptoMix family) spread via USPS-themed malspam, with tactics escalating to JavaScript downloaders dropping multiple malware families.
On April 11, 2017, a malspam campaign began distributing Mole ransomware, a CryptoMix-family strain, through USPS-themed emails linking to fake Microsoft Word Online sites offering a bogus Office plugin. By April 13 the campaign added Nemucod JavaScript downloaders installing Mole alongside Kovter and Miuref, and by April 18 it switched to the KINS banking Trojan before moving to speeding-ticket themes and a fake parking services website. Unit 42 notes the rapid tactic changes are likely intended to evade detection, and CryptoMix variants like Mole are typically repackaged within weeks.
Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks
Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.
The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.
Malicious LiteLLM Releases Tied to Trivy Hack May Have Exposed 2,100+ Organizations
Malicious LiteLLM 1.82.7/1.82.8 PyPI releases tied to the Trivy TeamPCP campaign harvested cloud, SSH, and database credentials, potentially exposing 2,500+ organizations.
CloudSEK reported that two malicious LiteLLM releases on PyPI (versions 1.82.7 and 1.82.8, live about 40 minutes on March 24) harvested cloud keys, SSH keys, Kubernetes tokens, and database passwords, with captured loot files mapping potential exposure to more than 2,500 organizations including NVIDIA, Cisco, Deloitte, Volkswagen, FedEx, Siemens, and X Corp. The campaign is part of TeamPCP (tracked by Google as UNC6780), linked to the Aqua Security Trivy scanner compromise tracked as CVE-2026-33634 and added to CISA's Known Exploited Vulnerabilities catalog on March 26. The payload used a litellm_init.pth file executed at Python interpreter startup and exfiltrated secrets to models.litellm[.]cloud; the FBI's FLASH-20260702-01 advisory urged rotation of CI/CD, publishing, and cloud credentials.