AI Agents Often Hold More System Permissions Than the Users They Serve

·1 views

AI agents and chatbots that operate across tools like Jira, GitHub, and AWS typically run under a single service account with broad access, meaning any user who can reach the agent can effectively perform actions beyond their own authorization. This creates a security gap where a user restricted from deleting branches, for example, can still trigger that action simply by asking the assistant. Prompt-level instructions telling the model to respect user permissions are insufficient, since authorization must be enforced in code before any tool call executes. Proposed solutions include per-user OAuth tokens, custom policy engines mirroring each system's rules, or querying each system's native permission APIs in real time before acting. A lightweight open-source tool called hallpass has been built to address this by checking live permissions against upstream systems and returning one of three explicit decisions — allow, deny, or unknown — rather than making assumptions.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Five AI Governance Gaps That Cause Real Production Incidents, Explained

A practitioner working in AI governance has identified five critical blind spots that most organizations overlook when managing AI systems in production. These include undetected shadow AI deployments, sensitive data leaking through prompts before logs can capture it, and sub-agents gaining capabilities beyond their intended scope. Two additional gaps involve delegated credentials that outlive their authorized timeframe and audit logs that cannot be independently verified by regulators. The author argues that most governance tools focus on individual system components rather than the connections between them, which is where unauthorized AI activity tends to occur.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

TypeSafe AI Launches Jev, a Decision-Only Model Built to Never Generate Text

TypeSafe AI, a startup founded by a former ChatGPT contributor, has released Jev, an AI model designed exclusively to make classifications and decisions rather than generate conversational text. Unlike ChatGPT, Claude, or Gemini, Jev does not produce responses word by word; instead, it instantly selects from a predefined set of answers and returns a confidence score. This makes it reportedly dozens of times faster and significantly cheaper for repetitive decision tasks such as spam filtering, ticket routing, or content moderation. The model's constrained output format also means it cannot fabricate information or deviate from the choices it is given. Developers see Jev not as a replacement for conversational AI but as a complementary tool that handles background judgment calls while full language models focus on tasks requiring explanation or reasoning.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

ShipFast, supastarter, SaaS Pegasus, GoVueKit: How Four SaaS Starters Compare

A detailed architectural comparison of four popular SaaS starter kits — ShipFast, supastarter, SaaS Pegasus, and GoVueKit — highlights how each targets a distinct developer profile and deployment model. ShipFast is a Next.js-based kit aimed at solo makers who need a fast path to a landing page with payments, while supastarter offers a full-stack TypeScript solution suited for B2B teams using Node or serverless environments. SaaS Pegasus caters to Python teams with a mature Django monolith, Celery background jobs, and features like impersonation and a CMS, whereas GoVueKit packages a Go API and Vue 3 frontend into a single self-hostable binary with PostgreSQL or SQLite. Key differentiators include multi-tenancy support, background job handling, test coverage, and data portability — with GoVueKit and Pegasus offering the most verifiable end-to-end test suites. The comparison advises teams to choose primarily based on backend language familiarity, need for organizations on day one, hosting constraints, and long-term data portability requirements.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Go developer unpacks channel internals after reading runtime source code

A developer spent a week studying Go 1.26's runtime source files chan.go and select.go to understand how channels work under the hood. Every Go channel is backed by an hchan struct containing a ring buffer, two wait queues for blocked goroutines, and a single runtime mutex that protects all operations. Unbuffered channels carry no buffer at all, requiring sender and receiver to meet simultaneously, with the sender writing a value directly onto the receiver's stack. When a receiver is already waiting, a buffered channel skips the buffer entirely and hands the value over directly, maintaining a strict invariant that data and waiting receivers never coexist. The study also revealed how Go preserves FIFO ordering when a full buffered channel has parked senders, by routing the oldest buffered value to the receiver and placing the sender's value into the freed slot.

0 comments Read more at DEV Community