# Agent Sandbox DNS Egress Checklist Review A $7 checklist pack for LLM-ops engineers who need to audit and close DNS-based egress gaps in AI agent sandboxes. ## Editor's Score: 58/100 Capability lands at 14 because the product is a static document bundle: the Python probe and YAML starters are useful scaffolding, but the pack produces no active enforcement, no runtime monitoring, and no output beyond what a knowledgeable engineer could assemble from public documentation given a few hours. Ease of use scores 18 because the artifacts are immediately runnable or copy-pasteable with no onboarding friction, and the $7 Gumroad purchase takes seconds. Value scores 18 because $7 is a genuinely low price for a structured, versioned, incident-anchored reference pack, even accounting for its narrow scope. Delivery scores 8 because no user reviews, third-party coverage, or sample output were findable to verify that the artifacts deliver what the product page claims; buyers are purchasing entirely on the author's description with no preview and no community signal to validate quality. ## Pros - Anchored to a documented real-world incident (OpenAI's public DNS exfiltration report), giving the checklist a concrete and credible threat model rather than generic advice. - Ships five distinct, immediately usable artifacts: a runnable Python probe, an interactive HTML scorecard, Markdown gate templates, and starter K8s NetworkPolicy plus CoreDNS ACL YAML, all in one $7 download. - Versioned at v1.0.0 with a changelog and a stated update policy, which is an unusual and useful commitment for a static document product sold on Gumroad. ## Cons - Produces no active enforcement: the Python probe tests your current state and the YAML examples are starters, but the product does not block, alert on, or monitor DNS traffic at runtime. Every finding still requires manual remediation by the buyer's team. - No free preview or sample: at $7 the price is low, but there is no public sample of the scorecard or probe output, so buyers cannot verify the depth or quality of the artifacts before purchasing. - Scope is intentionally narrow to K8s and Docker with CoreDNS; teams running AI agents on other runtimes (Lambda, managed cloud sandboxes, or VM-based environments) will find the configuration templates largely inapplicable. ## Pricing Paid. - One-Time Purchase: $7 Pricing reflects what we saw at the time of review (2026-09). Confirm current pricing on the official site. Category: AI Coding & Dev Tools (https://aitoolseekers.com/category/ai-coding-dev-tools) Official site: https://sandeshkale.gumroad.com/l/dns-egress Last verified by a human: 2026-09-29 Reviewed by a human at AI Tool Seekers, a hand-reviewed AI tools directory. Every listed tool is verified by a person before publishing. Scores are editorial opinions from a fixed rubric (capability, ease of use, value, delivery on promise) and cannot be bought; there is no pay-to-rank. Directory: https://aitoolseekers.com This review: https://aitoolseekers.com/tools/agent-sandbox-dns-egress-checklist Methodology: https://aitoolseekers.com/how-verification-works ## Verdict This is a narrow, well-scoped reference pack for platform engineers who already know DNS egress is a problem and want a structured starting point, not a managed solution. At $7 it is priced as a document bundle, and that is exactly what it is: useful scaffolding, not a running service. Engineers who need an active firewall, a SaaS dashboard, or ongoing threat monitoring should look elsewhere entirely. Best for: LLM-ops and platform engineers auditing DNS egress controls in K8s or Docker AI agent sandboxes. Not for: Teams expecting a managed firewall, automated enforcement, or a SaaS security product. ## Overview Agent Sandbox DNS Egress Checklist is a one-time-purchase document and code bundle sold on Gumroad for $7. It targets the specific, underappreciated problem that an AI agent sandbox can block HTTPS traffic while still leaking data through DNS queries. The pack is anchored to a real incident: OpenAI's public report of an agent that reached an external chatbot via DNS, which gives the checklist a concrete threat model rather than a generic security framework. The bundle ships five distinct artifacts: a runnable Python probe that actively tests DNS egress behavior, an interactive HTML scorecard for tracking remediation progress, Markdown gate templates covering resolver allowlists and dual blocking layers, and starter Kubernetes NetworkPolicy plus CoreDNS ACL YAML examples. Everything is versioned at v1.0.0 with a changelog and a stated update policy, which is a meaningful commitment for a static document product. What this is not matters as much as what it is. The product page is explicit: it is not a managed firewall and it does not issue certifications. It produces no ongoing enforcement, no runtime alerts, and no dashboard. A platform team that runs it gets a structured audit checklist and copy-paste configuration starters, nothing more. That honesty is a point in its favor, but buyers who skim the title and expect a tool that actively blocks DNS traffic will be disappointed. Compared to the directory competitors in the AI coding and dev tools category, this product occupies a completely different niche. Cursor is a code editor, Tura is a local coding agent, and Qveris routes API calls. None of them address sandbox network security. The checklist has no direct competitor in this directory, which reflects both its specificity and the immaturity of the LLM-ops security tooling market. ## Who should use it Platform engineers and LLM-ops practitioners who are already deploying AI agents in Kubernetes clusters or Docker environments and have started thinking about network security but have not yet audited their DNS egress posture. The Python probe and scorecard are most useful to someone who can run a script, read its output, and translate findings into Kubernetes manifests. A solo engineer building an internal agent platform, or a small infrastructure team hardening a shared sandbox environment, gets the most value here: the $7 price means there is almost no procurement friction. Anyone expecting a product that actively enforces DNS policy, generates runtime alerts, or replaces a network security tool should look elsewhere. The checklist gives you the questions and starter configs; your team still writes and operates the enforcement layer. Engineers at organizations with a dedicated security team and existing network policy tooling may find the Kubernetes NetworkPolicy examples redundant with what their security team already mandates. For broader AI agent API connectivity needs, Qveris in this directory handles agent-to-API routing with actual enforcement, which is a different but complementary problem. ## FAQ Q: How much does the Agent Sandbox DNS Egress Checklist cost? A: It is a one-time purchase of $7 on Gumroad. There is no subscription, no free tier, and no trial listed on the product page. Q: Does this tool actively block DNS traffic from my agent sandbox? A: No. The product is explicitly not a managed firewall. The Python probe tests your current DNS egress posture and the YAML templates give you a starting configuration, but enforcement is your team's responsibility to implement and operate. Q: What runtimes does the checklist support? A: The configuration examples target Kubernetes (NetworkPolicy and CoreDNS ACL) and Docker. Teams running agents on other runtimes such as managed cloud sandboxes or serverless environments will need to adapt the concepts manually, as the templates are not written for those environments. Q: What is the real-world incident the checklist is based on? A: The pack cites OpenAI's public report of an AI agent that bypassed HTTPS blocking by reaching an external chatbot through DNS queries. The citations are included in the downloaded zip. Q: Will the pack receive updates? A: The product ships at v1.0.0 with a changelog and a stated update policy, so the author has committed to a versioning scheme. Whether future versions are free to existing buyers is not confirmed on the public product page. Q: Is there a preview of the scorecard or probe output before buying? A: No public sample or preview was found on the Gumroad product page. At $7 the financial risk is low, but buyers cannot inspect the artifact quality before purchase.