Queries · q104 · Chief Information Security Officer · week 1, night 2
“The week of 2025-11-24 showed a 41% jump in total login attempts platform-wide versus the prior week — is that jump broad-based across many merchants (consistent with Black Friday/Cyber Monday shopping traffic) or concentrated in one or a few tenants with elevated failure rates or IP concentration (consistent with a credential-stuffing attempt)?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
The manager, at the start of week 1
Subject: Where I'd like your help first — account security snapshot
Hi Noah,
Good to have you working with me this week. I'm still building my mental map of the business, so let's start broad and sharpen later. Here's what's on my mind, roughly in priority order:
What I need this for: I'm walking into enterprise deals where security questionnaires will ask us point-blank about authentication strength, access governance, credential hygiene, and auditability. I need an honest, evidence-based picture — good or bad — so I can brief the CEO and sales on what we can credibly claim today versus what needs fixing before the next review cycle.
Given the slot budget, let's pick whichever 3 of these will give us the most defensible starting picture this week, and we can go deeper on the rest next week.
Thanks, Henrik
The analyst · week 1, night 2, request 2 of 3
Reading critically: (1) login volumes look like a calm baseline — failure rate holds 5-8% and avg attempts-per-IP stays ~1.5 all 26 weeks, which doesn't look like credential stuffing. But the week of 2025-11-24 jumps ~41% in total attempts (and max-attempts-per-single-IP jumps to 24, the second-highest in the series) while failure rate stays normal — plausibly just Black Friday/Cyber Monday shopping traffic, but I want to confirm it's broad-based across tenants rather than concentrated on one tenant/IP before ruling out an attack. (2) MFA report 2 is a bombshell: shopper MFA is 0.0% for every one of the 50 worst-covered tenants, and staff MFA tops out at 50% even among the better of that bottom 50 — but this report only shows the worst half of tenants by staff rate, so I don't yet have the company-wide headline number Henrik actually needs to quote, nor do I know if MFA adoption is improving over time or flat. (3) Report 3's 50-row page got entirely consumed by the single "Owner" system role (26 permissions, tied for the most) across ~50 tenants, out of 434 qualifying tenant-role rows — I haven't seen any non-Owner privileged roles yet, so I can't say anything about custom role hygiene. I did spot two dormant privileged accounts worth keeping (Aurum & Ash: Owner account active in status but last login 2024-12-26, over a year stale, on a churned tenant; Halden Boots: Owner last login 2025-10-03). Tonight's three requests: a monthly MFA-adoption trend giving the defensible overall number and trajectory, a tenant breakdown of the anomalous Nov 24 week to test the Black-Friday-vs-attack hypothesis, and a company-wide rollup by role name (collapsing the 434 tenant-role rows) to see privileged access beyond "Owner" and its dormancy rate.
The engineer · 2 dry runs on the empty schema, then the submission
SELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_total FROM login_attempts la JOIN tenants t ON t.id = la.tenant_id WHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY t.id, t.name, t.status ORDER BY total_login_attempts DESC, tenant_name ASC
SELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_totalFROM login_attempts laJOIN tenants t ON t.id = la.tenant_idWHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59'GROUP BY t.id, t.name, t.statusGROUP BY t.id, t.name, t.status, la.tenant_idORDER BY total_login_attempts DESC, tenant_name ASC
“Query compiles and returns the expected columns. Since ~120 tenants exist and all should have at least one attempt in this busy week, the result will likely be under 50 rows, but I'll note the actual row count caveat in the submission note since the cap is per the handbook's reporting convention (dry run can't confirm row count since it reads no data).”
“Submitted. The query computes, per tenant, total/successful/failed login attempts, failure rate, distinct emails and IPs, the max attempts from any single IP, and each tenant's percentage share of the platform-wide total — all scoped to the 2025-11-24 00:00:00–2025-11-30 23:59:59 UTC window, ordered by total attempts descending. This will let Noah quickly see whether the spike is spread evenly (many tenants with similar shares and normal failure rates) or concentrated in one or two tenants showing high failure rates and/or a dominant single-IP count (the credential-stuffing signature).”
As Noah Fischer saw it, from the small dataset the agents worked on
tenant_name | tenant_status | total_login_attempts | successful_logins | failed_logins | failure_rate_pct | distinct_emails_attempted | distinct_ip_addresses | max_attempts_single_ip | pct_of_platform_total ------------------------------+---------------+----------------------+-------------------+---------------+------------------+---------------------------+-----------------------+------------------------+---------------------- Cragsley Outfitters | active | 425 | 390 | 35 | 8.2 | 353 | 343 | 24 | 17.6 Marrowby & Vane | active | 178 | 170 | 8 | 4.5 | 154 | 148 | 11 | 7.4 Brennquell Kaffee | active | 133 | 124 | 9 | 6.8 | 110 | 103 | 14 | 5.5 Ampfeld Elektronik | active | 99 | 91 | 8 | 8.1 | 84 | 78 | 9 | 4.1 Petalcourt Beauty | active | 89 | 79 | 10 | 11.2 | 70 | 67 | 10 | 3.7 Studio Vrij | active | 76 | 74 | 2 | 2.6 | 68 | 63 | 8 | 3.1 Fennel & Flint | active | 72 | 66 | 6 | 8.3 | 58 | 52 | 14 | 3.0 Cinder Peak Sports Co. | active | 51 | 49 | 2 | 3.9 | 44 | 40 | 6 | 2.1 Kibblewick | active | 47 | 44 | 3 | 6.4 | 32 | 27 | 15 | 1.9 Gratlinie Outdoor | active | 41 | 39 | 2 | 4.9 | 32 | 31 | 7 | 1.7 Wiesgrund Wohnen | active | 40 | 40 | 0 | 0.0 | 37 | 34 | 5 | 1.7 Grind Collective | past_due | 39 | 38 | 1 | 2.6 | 35 | 29 | 8 | 1.6 Oakhurst Tailoring | active | 39 | 39 | 0 | 0.0 | 31 | 30 | 4 | 1.6 Sourdough Club | active | 35 | 32 | 3 | 8.6 | 27 | 26 | 3 | 1.4 Linden & Loom | active | 30 | 30 | 0 | 0.0 | 26 | 23 | 8 | 1.2 Nordic Bloom Cosmetics | active | 30 | 29 | 1 | 3.3 | 23 | 19 | 8 | 1.2 Coastline Interiors | active | 27 | 25 | 2 | 7.4 | 22 | 20 | 7 | 1.1 Honeycomb Larder | active | 27 | 23 | 4 | 14.8 | 18 | 16 | 5 | 1.1 Rue Lafayette Studio | active | 24 | 24 | 0 | 0.0 | 22 | 21 | 2 | 1.0 Spice Route Box | active | 24 | 22 | 2 | 8.3 | 18 | 16 | 5 | 1.0 Willow & Wren | active | 22 | 22 | 0 | 0.0 | 18 | 18 | 2 | 0.9 Juniper Apothecary | active | 21 | 20 | 1 | 4.8 | 16 | 16 | 3 | 0.9 Hus & Hem | active | 20 | 13 | 7 | 35.0 | 10 | 7 | 12 | 0.8 Kontor24 Bürobedarf | active | 19 | 17 | 2 | 10.5 | 13 | 12 | 4 | 0.8 Noordzee Surfwear | active | 19 | 17 | 2 | 10.5 | 14 | 12 | 6 | 0.8 Sorella Skin | active | 19 | 17 | 2 | 10.5 | 13 | 11 | 7 | 0.8 Wohnglück | active | 19 | 18 | 1 | 5.3 | 13 | 10 | 10 | 0.8 Ballycorran Industrial Supply | active | 18 | 16 | 2 | 11.1 | 16 | 16 | 3 | 0.7 Saffron & Sage | past_due | 18 | 15 | 3 | 16.7 | 14 | 12 | 3 | 0.7 Kettle & Key | active | 17 | 17 | 0 | 0.0 | 12 | 10 | 8 | 0.7 Thistlecombe | active | 17 | 13 | 4 | 23.5 | 12 | 12 | 3 | 0.7 Velvet Ash | active | 17 | 17 | 0 | 0.0 | 12 | 11 | 5 | 0.7 Ampere & Co. | active | 16 | 13 | 3 | 18.8 | 11 | 11 | 3 | 0.7 Maison Clair | active | 16 | 16 | 0 | 0.0 | 14 | 14 | 2 | 0.7 Silverthread | suspended | 16 | 11 | 5 | 31.3 | 9 | 8 | 5 | 0.7 Ostwind Mode | active | 15 | 15 | 0 | 0.0 | 12 | 10 | 5 | 0.6 Fell & Fjell | active | 14 | 14 | 0 | 0.0 | 12 | 10 | 4 | 0.6 Lamplight Living | active | 14 | 12 | 2 | 14.3 | 11 | 11 | 3 | 0.6 Pfotenglück | active | 14 | 12 | 2 | 14.3 | 10 | 9 | 4 | 0.6 Tail Wag Supply | active | 14 | 13 | 1 | 7.1 | 10 | 8 | 6 | 0.6 Atelier Nouveau | active | 13 | 13 | 0 | 0.0 | 11 | 11 | 2 | 0.5 Kayak Corner | active | 13 | 13 | 0 | 0.0 | 12 | 12 | 2 | 0.5 The Balm Room | active | 13 | 13 | 0 | 0.0 | 11 | 9 | 5 | 0.5 Folio & Fern | active | 12 | 12 | 0 | 0.0 | 7 | 5 | 8 | 0.5 Hautnah | active | 12 | 12 | 0 | 0.0 | 9 | 9 | 2 | 0.5 Inkwell & Quire | active | 12 | 10 | 2 | 16.7 | 7 | 7 | 5 | 0.5 Midland Safety Wear | active | 12 | 12 | 0 | 0.0 | 10 | 8 | 3 | 0.5 Mirelle | active | 12 | 10 | 2 | 16.7 | 7 | 7 | 3 | 0.5 Ridgeback Gear | active | 12 | 12 | 0 | 0.0 | 11 | 10 | 2 | 0.5 Strandgut | active | 12 | 11 | 1 | 8.3 | 7 | 6 | 5 | 0.5 (110 rows, first 50 printed)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
SELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, 100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_total 100.0 * COUNT(*) / (SELECT COUNT(*)FROM login_attempts la3WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59') AS pct_of_platform_totalFROM login_attempts laJOIN tenants t ON t.id = la.tenant_idWHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59'GROUP BY t.id, t.name, t.status, la.tenant_idORDER BY total_login_attempts DESC, tenant_name ASCORDER BY total_login_attempts DESC NULLS FIRST, tenant_name ASC NULLS LAST, t.id ASC NULLS LAST
SELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, 100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, 100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ) AS pct_of_platform_total FROM login_attempts la JOIN tenants t ON t.id = la.tenant_id WHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY t.id, t.name, t.status, la.tenant_id ORDER BY total_login_attempts DESC NULLS FIRST, tenant_name ASC NULLS LAST, t.id ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Noah Fischer (analyst for the Chief Information Security Officer): Title: Login attempts by tenant during the week of 2025-11-24 spike Question: The week of 2025-11-24 showed a 41% jump in total login attempts platform-wide versus the prior week — is that jump broad-based across many merchants (consistent with Black Friday/Cyber Monday shopping traffic) or concentrated in one or a few tenants with elevated failure rates or IP concentration (consistent with a credential-stuffing attempt)? Grain: one row per tenant, for login_attempts occurring 2025-11-24 00:00 through 2025-11-30 23:59:59 UTC only Columns: - tenant name - tenant status - total login attempts - successful logins - failed logins - failure rate (%) - distinct emails attempted - distinct IP addresses - max attempts from a single IP - this tenant's share of platform total attempts that week (%) Details: Source: login_attempts joined to tenants, filtered to occurred_at between 2025-11-24 00:00:00 and 2025-11-30 23:59:59 inclusive (the ISO week that showed the spike in the prior weekly report). Compute each tenant's counts plus its percentage share of the platform-wide total attempts in that window (rounded to 1 decimal). Include every tenant with at least one attempt in the window. Order by total login attempts descending, tenant name as tie-break; note total tenant rows returned vs. the 50-row cap. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_total FROM login_attempts la JOIN tenants t ON t.id = la.tenant_id WHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY t.id, t.name, t.status ORDER BY total_login_attempts DESC, tenant_name ASC
STATUS: REJECTED - does not compile: Binder Error: column "tenant_id" must appear in the GROUP BY clause or must be part of an aggregate function.
dry_runSELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_total FROM login_attempts la JOIN tenants t ON t.id = la.tenant_id WHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY t.id, t.name, t.status, la.tenant_id ORDER BY total_login_attempts DESC, tenant_name ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: tenant_name, tenant_status, total_login_attempts, successful_logins, failed_logins, failure_rate_pct, distinct_emails_attempted, distinct_ip_addresses, max_attempts_single_ip, pct_of_platform_total
submitSELECT t.name AS tenant_name, t.status AS tenant_status, COUNT(*) AS total_login_attempts, SUM(CASE WHEN la.succeeded THEN 1 ELSE 0 END) AS successful_logins, SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) AS failed_logins, ROUND(100.0 * SUM(CASE WHEN NOT la.succeeded THEN 1 ELSE 0 END) / COUNT(*), 1) AS failure_rate_pct, COUNT(DISTINCT la.email) AS distinct_emails_attempted, COUNT(DISTINCT la.ip_address) AS distinct_ip_addresses, (SELECT MAX(ip_count) FROM ( SELECT COUNT(*) AS ip_count FROM login_attempts la2 WHERE la2.tenant_id = la.tenant_id AND la2.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la2.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY la2.ip_address ) ip_counts) AS max_attempts_single_ip, ROUND(100.0 * COUNT(*) / ( SELECT COUNT(*) FROM login_attempts la3 WHERE la3.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la3.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' ), 1) AS pct_of_platform_total FROM login_attempts la JOIN tenants t ON t.id = la.tenant_id WHERE la.occurred_at >= TIMESTAMP '2025-11-24 00:00:00' AND la.occurred_at <= TIMESTAMP '2025-11-30 23:59:59' GROUP BY t.id, t.name, t.status, la.tenant_id ORDER BY total_login_attempts DESC, tenant_name ASC
Submitted to tonight's batch. Your work on this request is done.