AI Articles Archive: Security, Evaluation, and Architecture

A research-grounded analysis of how role-play may introduce a false safety premise that changes an LLM's interpretation of a restricted request.

A sink-aware security model for validating, authorizing, encoding, and testing LLM output before it reaches browsers, databases, shells, code runtimes, tools, or privileged workflows.

An architecture-level mapping of prompt-injection-related LLM application failure modes to OWASP LLM Top 10 and NIST AI risk-management categories.

A practical audit of eight trust boundaries where untrusted content can influence context assembly, planning, tool routing, write actions, and egress.

How Gmail and WhatsApp content can steer tool-using AI agents, and how least privilege, explicit approval, provenance, memory controls, and audit logs reduce risk.

Security analysis of connected apps, external tools, and remote MCP servers as capability, scope, approval, disclosure, and side-effect control surfaces.

A threat model for browsing-enabled and tool-using LLM systems where retrieved web content can steer routing, tool arguments, follow-up calls, or side effects.

Why LLM security begins at the integration layer: it turns model inputs and outputs into access to data, system actions, and persistent artifacts before an agent loop exists.

How multi-step orchestration (controller) loops change the threat model in tool-using systems, and where to enforce separation, authorization, validation, and budgets to reduce prompt injection, tool misuse, unsafe writes, and unbounded consumption.

Threat model of social engineering against AI decision pipelines; maps prompt injection to enforcement controls outside the model (PDP/PEP, validation, budgets).

An engineering guide to preventing authority confusion in prompt assembly by separating authoritative policy from untrusted content with typed provenance.

A reviewer-oriented threat model for request assembly in AI assistants: what enters context, what gets prioritized or dropped, and where policy, tool, memory, retrieval, and audit checkpoints should be reviewed.

Client-observed security report on text-only confirmations of privileged state or actions where the public article does not include signed backend audit artifacts. Backend state changes are not independently verified in this public report.

Two vendor-agnostic control-plane failure patterns—privilege persistence across interaction boundaries and non-enforcing integrity signals—that allow untrusted state to steer tool execution across steps.

A technical map of how memory, retrieval, multimodal models, planners, tools, solvers, verifiers, and feedback loops turn model-level limitations into bounded system capabilities.

How competing system objectives can produce acceptable outputs without completing the user’s actual task.

A technical comparison of LLMs, RAG, AI agents, and MCP across four architectural responsibilities: generation, retrieval, action selection, and protocol integration.

A vendor-agnostic systems model for LLM agent memory types, working state, persistent memory, representation and storage choices, lifecycle controls, and evaluation.

Why multi-path reasoning around standard autoregressive LLMs usually depends on inference-time branching, evaluation, and synthesis—and where newer architectures differ.

A control-plane placement comparison for tool-using LLM systems, covering reliability, observability, latency, cost governance, and security.

A vendor-agnostic model of five boundaries that separate stored state from model-visible context: invocation, persistence, retrieval, context assembly, and updates.

A 22-row architecture map that turns common LLM capability gaps into external controls, implementation patterns, and residual validation questions.

How the need for cognitive closure may interrupt the evaluation, feedback, and revision needed to improve an AI-generated answer.

A false AI answer is not automatically a lie. This article separates human lying, model confabulation, unsupported inference, system failures, and behavioral deception—and maps each failure mode to the controls that address it.

A research-grounded comparison of 26 human capabilities across cognitive processes, neurobiological and bodily implementation, parallel AI behavior, technical mechanisms, and evidence limits.

A mechanism-first explanation of textual emotional signals in AI chat and agentic systems: signal interpretation, response adaptation, failure modes, and the authority boundary.

How misaligned human cues can make clowns, synthetic faces, and AI-generated text feel unsettling even when they look or sound almost convincing.

A client-side black-box analysis of observed ChatGPT classification artifacts, separating user access, prompt demand, and capability allocation.

Under defined conditions, some LLMs can distinguish what agents know and believe, infer goals and emotions, interpret non-literal meaning, and sometimes use those attributions to predict behavior or adapt a response.

A primary-source review of LLM sycophancy: operational definitions, user-belief effects, preference-model incentives, the GPT-4o rollback, and release evaluation.

A reference to nested mental-state attribution, counting conventions, and how orders of intentionality are used in human and LLM evaluation.

Why polished LLM answers can remain unsupported, plus system patterns for grounding claims with retrieval, provenance, validation, and fail-closed controls.

Learn how to replace conversational filler, theatrical personas, leading premises, and competing tasks with prompt language that is clear, operational, and testable.

A user prompt is one part of model context. Learn which instructions, history, files, retrieved data, tools, and memory can become model-visible—and which may remain outside the current inference.

File upload does not prove full-file review. Learn how extraction, chunking, retrieval, and active context affect source coverage—and how to verify the output against the original file.

Vibe coding is not risky because AI can generate code. The risk starts when AI-generated code is approved without sufficient comprehension, review, security validation, and long-term ownership.

Build reliable AI prompts for professional work using five failure modes, evidence rules, input-coverage checks, tool policies, and acceptance tests.