How to Use LLMs for Free: OpenCode 2 Built-in Models and OpenRouter
Install OpenCode 2, select a built-in free model, and complete your first everyday task. Connect OpenRouter later if you want more models or a free API for your own application.
ai
1264 postsAI Common Failures: Contrast Framing, Significance Inflation, Indicative Summaries, Overgeneralization
LLM prose has four recurring failures: contrast framing (refuting an X nobody said), significance inflation (dressing a plain fact up as a turning point), indicative summaries (announcing the topic, withholding the conclusion) and overgeneralization (summaries that drop conditions and scope). Fix the first three by cutting the filler and writing the answer; fix the fourth by checking each sentence against the source. Contrast framing has been traced to preference data, and models revising a draft move it into another contrast form, so banning a phrase in the prompt may not stop it.
Which Layers Your Agent Should Worry About: Priorities for Personal, Team, and Multi-Tenant Setups
In my judgment, OpenAI wrote about a research environment for training frontier models; for a personal agent, limiting credentials and exits comes first, and covert channels become a main risk only once there is a second tenant that must be isolated.
How to Judge a Security Tool's Claims: OWASP Lists, Benchmarks and a Checklist for 'Merged into a Big Vendor'
When you see 'covers OWASP 10 of 10', 'merged into Microsoft and Cisco' or '9X% detection', ask five things: who said it, what the term means, who set the questions, what is being measured, and where the primary source is. The earlier posts and a few cases added here each happen to show one: Microsoft's toolkit itself says 7 full and 3 partial; of ATR's (an open-source set of agent threat-detection rules) merged PRs into Microsoft and Cisco, the three checked were all opened by its author; Cisco's own detection rate and an independent study's differ several-fold; common benchmarks score models or agents, not tools.
Can an AI “False Positive” Decision Close an Alert?
Define what the alert detects before separating benign triggers, blocked attacks, and insufficient evidence. An assistant can propose evidence-linked triage; closure requires predefined criteria.
Connecting an attack agent: planning, execution, and feedback
Build a bounded HTTP testing loop with explicit planner, executor, verifier, trace, and stop conditions.
Can AI compose an attack chain? From CTFs to a public incident
Replay a ticket attack chain and analyze Anthropic’s public incident report, separating evidence, human decisions, evaluation shortcuts, and visibility limits.
How AI turns a target into an attack surface
Turn ticket-service documentation and HTTP observations into a source-linked attack surface and choose the next minimal test.
AI Code Review: Turn Suspicious Code into a Testable Finding
Trace identity, object selection, and the return path before testing allowed and forbidden pairs. Review comments propose hypotheses; an independent policy and observed output establish the finding.
Choose an AI Security Practice Environment: Local, Cloud, CTF, or Authorized Targets
Use a resettable copy to examine application logic, preserve cloud conditions to examine deployment boundaries, and follow the agreed scope for real systems. Keep the question and outcome criteria when changing environments, and record what changed.
How to verify an AI-generated exploit test
Replay HTTP exploit requests against vulnerable and fixed services, verify exact resource content, and reject status-only success claims.
Who Can Read Which Data After Login? Identity and Authorization for AI Assistants
The backend derives the acting identity from trusted authentication, then checks the requested action against the resource. Test an explicit same-tenant, owner-only policy with allowed and denied reads.
Build an AI-Assisted Incident Timeline Without Inventing Missing Events
Normalize time without discarding provenance. One ticket read establishes that read; an assistant claim, missing logs, and a later denial cannot substitute for the underlying evidence.
Where should prompt-injection defenses live?
Replay one malicious tool proposal through different execution paths and distinguish model manipulation from unauthorized effects. Labels, data permissions, and action approvals serve different purposes.
An Integrated AI Security Exercise: From Attack Evidence to a Fix and Regression
Compare vulnerable and fixed builds with the same HTTP requests, retaining ticket responses, audit events, and a timeline. Block unauthorized reads while preserving legitimate access.
MCP trust boundaries: what exactly did you trust?
Verify tool provenance, complete definitions, token audience, and downstream actions separately. A manifest hash detects metadata changes, not hidden remote behavior changes.
How model output becomes XSS, commands, or data exfiltration
Identify the consumer before validating output. HTML, URLs, and operation arguments require different controls; valid JSON, escaping, and authorization are separate properties.
Learning AI Security: Practical Paths Through Attacks, Defense, and Investigation
Follow one support assistant through AI-assisted vulnerability discovery, protection of AI data and tools, and AI-assisted investigation and remediation. Each path produces reproducible evidence.
What Must You Verify After an AI Patch?
A patch must block unauthorized access, preserve legitimate behavior, and pass against the actual target. A cleared alert and a passing local copy each establish only part of that result.
How AI attacks another AI: indirect injection and tool misuse
Trace hostile attachments into tool requests and separate model susceptibility from backend data exposure using a live-LLM training lab and HTTP replay.