← All hunts high TLP:CLEAR Part 3 of 3

AI-Secret Theft and Ransomware Post-Exploitation

An adversary is targeting hosts with AI development tools to scrape keychain secrets, moving laterally using discovery tools before exfiltrating data via Rclone and deploying ransomware like VYPR or Interlock.

Based on research by Cisco Talos 2026-10-30 12 steps · 5 queries T1003 T1021.001 T1021.002 T1041 T1486 T1567.002

Brief

The Cisco Talos IR Trends Q3 2026 report (https://blog.talosintelligence.com/ir-trends-q3-2026/) describes a shift in adversary behavior, specifically the impersonation of IT help desks to help ransomware attacks. Once actors gain initial access, they often target high-value assets within the developer population. This hunt addresses the post-exploitation behaviors observed in these campaigns, where adversaries move from initial access to the theft of AI platform secrets and final ransomware deployment.

Phase 1: Scoping High-Value Targets

The hunt begins by examining the software inventory across the entire estate. It identifies hosts running AI development tools and CLI packages, including Anthropic, Claude-code, and GitHub Copilot. These tools often store session tokens or API keys in the local keychain. By identifying which workstations run these packages, the hunt narrows the focus to systems most likely to be targeted for secret theft. This initial scoping ensures that subsequent queries focus on the relevant part of the fleet.

Phase 2: Detecting Credential Scraping and Discovery

Once we identify the target hosts, the hunt examines process and script activity for two distinct behaviors. The first query analyzes the prevalence of discovery tools on the hb_process_activity surface. We look for binaries like PsExec, Advanced IP Scanner, or MSTSC that appear on fewer than five hosts. In a developer environment, the sudden appearance of these tools on a single machine often indicates lateral movement. Simultaneously, we query the hb_script_activity surface for macOS keychain access. We search for shell scripts that use the 'security' command to find generic or internet passwords associated with AI tool keywords. This identifies the automated harvesting of developer credentials.

Phase 3: Monitoring Exfiltration and Impact

The hunt then transitions to the terminal stages of the intrusion chain using the hb_process_activity and hb_file_activity surfaces. We monitor process activity for Rclone execution. Adversaries in the VYPR and Interlock campaigns use this tool to sync stolen data to external cloud storage. We look for command lines that include 'sync' or 'copy' operations. Finally, we scan file activity for the creation of known ransom notes, such as 'vypr_read_me.txt'. Detecting the note creation is the final indicator that an intrusion has reached its terminal stage.

Why This is a Hunt

This process is a hunt rather than a simple detection because it connects disparate behaviors across time and surfaces. While a single alert on a ransom note is useful, it often arrives too late. By correlating the presence of specific AI software with rare lateral movement and keychain scraping, we identify the intent of the adversary early in the lifecycle. This context allows for a high-confidence response, such as host isolation, before data is exfiltrated or encrypted.

Blind Spots

This hunt depends on telemetry from an EDR agent. It cannot observe activity on unmanaged systems that an adversary might use as a beachhead. Furthermore, the script-based query will not trigger if an attacker uses a custom compiled binary to access the macOS keychain. Analysts should also monitor for unusual network traffic to known cloud storage providers to supplement the Rclone detection.

In this series

Steps

  1. Scope hosts with AI development tools

    Query · scoping

    Identify hosts running tools that the adversary is specifically targeting for secret theft.

    reads hb_software_inventorysql
    SELECT device_hostname, package_name, package_version, vendor_name FROM hb_software_inventory WHERE (instr(',' || '{{ai_tool_names}}' || ',', ',' || LOWER(package_name) || ',') > 0)

    What a hit looks like. A list of developer workstations or servers where AI secrets might reside. Silence indicates no targeted software is present.

  2. Identify rare discovery tool usage

    Query · baseline

    Detect the use of lateral movement tools that are uncommon across the fleet.

    reads hb_process_activitysql
    SELECT LOWER(process_name) AS tool_name, COUNT(DISTINCT device_hostname) AS host_count, MIN(time) AS first_seen FROM hb_process_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (instr(',' || '{{discovery_tools}}' || ',', ',' || LOWER(process_name) || ',') > 0 OR instr(',' || '{{discovery_tools}}' || ',', ',' || LOWER(process_original_file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days') GROUP BY 1 HAVING host_count < 5 ORDER BY host_count ASC

    What a hit looks like. Discovery tools used on a small number of hosts. Silence proves no rare usage of these specific binaries.

  3. Search for macOS keychain scraping

    Query · triage

    Find scripts targeting AI platform secrets within the macOS keychain.

    reads hb_script_activitysql
    SELECT device_hostname, actor_user_name, script_content, time FROM hb_script_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (LOWER(script_content) LIKE '%security find-generic-password%' OR LOWER(script_content) LIKE '%security find-internet-password%') AND (LOWER(script_content) LIKE '%anthropic%' OR LOWER(script_content) LIKE '%claude%') AND time >= datetime('now', '-{{lookback_days}} days')

    What a hit looks like. Script blocks using native macOS commands to access the keychain for AI platform secrets. Silence indicates no such scripted scraping was detected.

  4. Assess early discovery and theft

    Agent triage

    Evaluate if identified hosts show both the targeted tools and suspicious discovery behavior.

  5. Detect Rclone exfiltration activity

    Query · baseline

    Identify the use of Rclone for exfiltrating data, as observed in VYPR campaigns.

    reads hb_process_activitysql
    SELECT LOWER(process_name) AS tool_name, device_hostname, process_cmd_line, COUNT(DISTINCT device_hostname) AS host_count, MIN(time) AS first_seen FROM hb_process_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (instr(',' || '{{rclone_keywords}}' || ',', ',' || LOWER(process_name) || ',') > 0 OR instr(',' || '{{rclone_keywords}}' || ',', ',' || LOWER(process_original_file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days') GROUP BY 1, 2, 3 HAVING host_count < 5 ORDER BY host_count ASC

    What a hit looks like. Rclone executions that are rare across the estate, specifically those involving sync or copy operations. Silence indicates no unusual Rclone usage was seen.

  6. Identify ransom note creation

    Query · detection candidate

    Find definitive evidence of ransomware deployment from VYPR or Interlock variants.

    reads hb_file_activitysql
    SELECT device_hostname, file_name, file_path, process_name, time FROM hb_file_activity WHERE activity_id = 1 AND (instr(',' || '{{ransom_notes}}' || ',', ',' || LOWER(file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days')

    What a hit looks like. The query detects the creation of known ransom note filenames on disk. Silence proves no known note filenames were created in the window.

  7. Synthesize intrusion chain

    Agent triage

    Determine if the observed behavior represents a completed intrusion chain from discovery to ransom.

  8. Route on final verdict

    Decision

    Direct the response based on the agent's confidence in a malicious intrusion.

  9. Isolate impacted endpoint

    Response action

    Halt exfiltration or encryption by isolating the compromised host.

  10. Perform manual remediation

    Analyst task

    Review findings and perform clean-up of persistence or credential theft.

  11. Close out hunt

    Analyst task

    Final documentation and evaluation for detection promotion.

Coverage

Scenario coverage

StageCoveredHow, or why not
Network discovery and lateral movement
T1021.002 · T1021.001
Yes discovery-prevalence
macOS keychain scraping for AI credentials
T1003
Yes keychain-details
Data exfiltration via Rclone
T1567.002 · T1041
Yes exfiltration-rclone
Ransomware encryption
T1486
Yes ransom-impact
IT help desk vishing and remote assistance
T1566 · T1133
Out of scope Belongs to another part of the 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases' series.
Exploitation of Cisco IOS XE management interface
T1190
Out of scope Belongs to another part of the 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases' series.
Web shell implant and privileged account creation
T1505.003 · T1133
Out of scope Belongs to another part of the 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases' series.
RMM deployment and MSI installation
T1059.001
Out of scope Belongs to another part of the 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases' series.
EDR killing via BYOVD and task termination
T1562.001 · T1070.001
Out of scope Belongs to another part of the 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases' series.

Blind spots

  • Needs EDR agent presence. A host without an agent provides no telemetry for process or script activity, allowing the actor to operate unobserved. It would answer Whether the hunt missed unmanaged systems used as beachheads.
  • Needs Native macOS keychain auditing. If the attacker uses a compiled binary to access the keychain, it will not appear in hb_script_activity, only as a process name in hb_process_activity. It would answer Whether the keychain was scraped via a direct binary call instead of a script block.

Parameters & data

Parameters

ParameterTypeDefaultWhat it is
ai_tool_nameslist[string]github-copilot, claude-code, anthropic, copilot-cliPackage names of targeted AI development tools.
discovery_toolslist[string]psexec.exe, psexec64.exe, mstsc.exe, advanced_ip_scanner.exe, ipscan.exeBinaries used for lateral movement and internal discovery.
lookback_daysnumber14Days of history to examine.
ransom_noteslist[string]vypr_read_me.txt, interlock_instructions.txt, read_me.txtFilenames of ransom notes identified in the report.
rclone_keywordslist[string]rclone, rclone.exe, rclone64.exeExecutable names associated with the Rclone exfiltration tool.
scope_hostslist[host]—Limit the hunt to specific hostnames; leave empty to scan the entire estate.

Telemetry

SourceCategoryTelemetry
Endpoint telemetry (hb_ surfaces)endpointendpoint

Source

Download hunt.md Definition (JSON) An open hunt.md file; it runs anywhere that reads the format.
---
analysis: A simple rule might catch a ransom note, but this hunt connects the targeted
  software (AI tools) to the behavior (keychain scraping and rare discovery tools)
  and the final outcome (Rclone exfiltration), providing the full context needed for
  a high-severity response.
blind_spots:
- id: no-agent-coverage
  question: Whether the hunt missed unmanaged systems used as beachheads
  requires: EDR agent presence
  risk: A host without an agent provides no telemetry for process or script activity,
    allowing the actor to operate unobserved.
- id: keychain-script-masking
  question: Whether the keychain was scraped via a direct binary call instead of a
    script block
  requires: Native macOS keychain auditing
  risk: If the attacker uses a compiled binary to access the keychain, it will not
    appear in hb_script_activity, only as a process name in hb_process_activity.
  stage: credential-access-ai-secrets
coverage:
- stage: lateral-movement-discovery
  status: covered
  steps:
  - discovery-prevalence
- stage: credential-access-ai-secrets
  status: covered
  steps:
  - keychain-details
- stage: exfiltration-over-c2
  status: covered
  steps:
  - exfiltration-rclone
- stage: data-encryption-for-impact
  status: covered
  steps:
  - ransom-impact
- reason: 'Belongs to another part of the ''IR trends Q3 2026: Actors impersonate
    IT help desks in vishing attacks as ransomware activity increases'' series.'
  stage: vishing-rmm-access
  status: out_of_scope
- reason: 'Belongs to another part of the ''IR trends Q3 2026: Actors impersonate
    IT help desks in vishing attacks as ransomware activity increases'' series.'
  stage: edge-vulnerability-exploitation
  status: out_of_scope
- reason: 'Belongs to another part of the ''IR trends Q3 2026: Actors impersonate
    IT help desks in vishing attacks as ransomware activity increases'' series.'
  stage: persistence-web-shell-accounts
  status: out_of_scope
- reason: 'Belongs to another part of the ''IR trends Q3 2026: Actors impersonate
    IT help desks in vishing attacks as ransomware activity increases'' series.'
  stage: rmm-dropper-execution
  status: out_of_scope
- reason: 'Belongs to another part of the ''IR trends Q3 2026: Actors impersonate
    IT help desks in vishing attacks as ransomware activity increases'' series.'
  stage: defense-evasion-edr-impairment
  status: out_of_scope
guardrails:
  claims: no_unsupported
  evidence: citation_required
  missing_data: not_benign
  telemetry: untrusted
hunt:
  applicability: campaign-specific
  handoff: promote-to-detection
  justification: The rise in targeted AI credential theft and immature but destructive
    ransomware like VYPR necessitates a proactive hunt across endpoints to ensure
    data integrity and prevent operational outages.
  methodology: model-assisted
  trigger: intel-report
hypothesis: An adversary is targeting hosts with AI development tools to scrape keychain
  secrets, moving laterally using discovery tools before exfiltrating data via Rclone
  and deploying ransomware like VYPR or Interlock.
labels:
- hunt
- attack.t1021.002
- attack.t1021.001
- attack.t1003
- attack.t1567.002
- attack.t1041
- attack.t1486
- credential access
- defense evasion
- execution
- exfiltration
- impact
- initial access
- lateral movement
- persistence
name: AI-Secret Theft and Ransomware Post-Exploitation
parameters:
  ai_tool_names:
    default:
    - github-copilot
    - claude-code
    - anthropic
    - copilot-cli
    description: Package names of targeted AI development tools.
    from:
      kind: article
      observed: '2026-10-29'
      ref: Talos Q3 2026 Report
    type: list[string]
  discovery_tools:
    default:
    - psexec.exe
    - psexec64.exe
    - mstsc.exe
    - advanced_ip_scanner.exe
    - ipscan.exe
    description: Binaries used for lateral movement and internal discovery.
    from:
      kind: article
      observed: '2026-10-29'
      ref: Talos Q3 2026 Report
    type: list[string]
  lookback_days:
    default: '14'
    description: Days of history to examine.
    from:
      kind: manual
      observed: '2026-10-29'
      ref: Standard lookback window
    type: number
  ransom_notes:
    default:
    - vypr_read_me.txt
    - interlock_instructions.txt
    - read_me.txt
    description: Filenames of ransom notes identified in the report.
    from:
      kind: article
      observed: '2026-10-29'
      ref: Talos Q3 2026 Report
    type: list[string]
  rclone_keywords:
    default:
    - rclone
    - rclone.exe
    - rclone64.exe
    description: Executable names associated with the Rclone exfiltration tool.
    from:
      kind: article
      observed: '2026-10-29'
      ref: Talos Q3 2026 Report
    type: list[string]
  scope_hosts:
    default: []
    description: Limit the hunt to specific hostnames; leave empty to scan the entire
      estate.
    from:
      kind: manual
      observed: '2026-10-29'
      ref: Analyst scoping
    type: list[host]
provenance:
  authors:
  - name: Huntbase hunt generation
    org: huntbase.io
  generated:
    by: huntbase-hunt-generation
    from: https://blog.talosintelligence.com/ir-trends-q3-2026/
    gates:
    - dry-run
    - lint
    model: hb_google/gemini-3-flash-preview
rationale: Focus on developer environments first, then expand to the general server
  estate if discovery tools are found outside the developer scope.
references:
- name: 'Talos IR Trends Q3 2026: Actors impersonate IT help desks'
  url: https://blog.talosintelligence.com/ir-trends-q3-2026/
related:
- hunt: rmm-access-social-engineering
  reason: Initial access via social engineering and RMM tools is covered by a precursor
    hunt.
  relation: out-of-scope-alternative
- hunt: edr-impairment-rmm-execution
  relation: follows
scenario:
  stages:
  - name: IT help desk vishing and remote assistance
    observables:
    - Microsoft Quick Assist
    - IT help desk impersonation
    - vishing calls
    slug: vishing-rmm-access
    tactic: initial-access
    techniques:
    - T1566
    - T1133
  - name: Exploitation of Cisco IOS XE management interface
    observables:
    - CVE-2023-20198
    - web-based management interface access
    slug: edge-vulnerability-exploitation
    tactic: initial-access
    techniques:
    - T1190
  - name: Web shell implant and privileged account creation
    observables:
    - BadCandy web shell
    - privilege 15 command
    - local user creation on network device
    slug: persistence-web-shell-accounts
    tactic: persistence
    techniques:
    - T1505.003
    - T1133
  - name: RMM deployment and MSI installation
    observables:
    - MeshAgent
    - PowerShell
    - MSI package
    - remote IP download
    slug: rmm-dropper-execution
    tactic: execution
    techniques:
    - T1059.001
  - name: EDR killing via BYOVD and task termination
    observables:
    - taskkill.exe
    - cscm.exe
    - sfc.exe
    - BYOVD
    - vulnerable drivers
    - Windows Defender real-time protection disabled
    - event logs cleared
    slug: defense-evasion-edr-impairment
    tactic: defense-evasion
    techniques:
    - T1562.001
    - T1070.001
  - name: Network discovery and lateral movement
    observables:
    - PsExec.exe
    - MSTSC
    - IP scan
    - Microsoft Terminal Services Client
    slug: lateral-movement-discovery
    tactic: lateral-movement
    techniques:
    - T1021.002
    - T1021.001
  - name: macOS keychain scraping for AI credentials
    observables:
    - macOS keychain
    - Anthropic Claude Code secrets
    - credential scraping scripts
    slug: credential-access-ai-secrets
    tactic: credential-access
    techniques:
    - T1003
  - name: Data exfiltration via Rclone
    observables:
    - Rclone
    - multi-day exfiltration
    slug: exfiltration-over-c2
    tactic: exfiltration
    techniques:
    - T1567.002
    - T1041
  - name: Ransomware encryption
    observables:
    - VYPR
    - Interlock
    - ransom note
    slug: data-encryption-for-impact
    tactic: impact
    techniques:
    - T1486
  summary: Threat actors utilized vishing to impersonate IT help desks and establish
    remote sessions via Microsoft Quick Assist, or exploited CVE-2023-20198 on Cisco
    IOS XE edge devices to gain entry. Post-compromise, they deployed RMM tools and
    vulnerable drivers to terminate EDR systems (BYOVD) before conducting multi-day
    data exfiltration via Rclone and deploying ransomware variants like VYPR and Interlock.
series:
  index: 3
  slug: ir-trends-q3-2026-actors-impersonate-it-help-desks-in-vishing-attacks-as-ransomware-activity-inc
  title: 'IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as
    ransomware activity increases'
  total: 3
severity: high
targets:
  analyst:
    name: Tier-2 analyst
    role: analyst
  endpoint:
    category: endpoint
    name: Endpoint telemetry (hb_ surfaces)
    telemetry:
    - endpoint
  hunter:
    agent: true
    name: Hunt agent
tlp: clear
type: investigation
---


# AI-Secret Theft and Ransomware Post-Exploitation

This hunt focuses on the post-exploitation phase of a ransomware intrusion, specifically targeting the theft of AI-related secrets (Anthropic/GitHub Copilot) and the subsequent exfiltration. It pivots from an initial software inventory scan for targeted AI tools into a phased analysis of lateral movement, credential scraping, and final impact indicators. By correlating these behaviors, the hunt identifies sophisticated actors who blend in with developer activities before executing the final ransomware payload.

## scoping-ai-tools
<!-- Scope hosts with AI development tools -->
Identify hosts running tools that the adversary is specifically targeting for secret theft.

```sqlite target=endpoint role=scoping params=(ai_tool_names=ai_tool_names)
~~~yaml
expected: A list of developer workstations or servers where AI secrets might reside.
  Silence indicates no targeted software is present.
reads:
- device_hostname
- package_name
- package_version
- vendor_name
silence: not_evidence_of_absence
source: hb_software_inventory
verified: dry-run
verified_at: '2026-10-30'
~~~
SELECT device_hostname, package_name, package_version, vendor_name FROM hb_software_inventory WHERE (instr(',' || '{{ai_tool_names}}' || ',', ',' || LOWER(package_name) || ',') > 0)
```

## parallel-early
<!-- Examine early post-exploitation -->
parallel:
- → discovery-prevalence
- → keychain-details
join: → early-triage

## discovery-prevalence
<!-- Identify rare discovery tool usage -->
Detect the use of lateral movement tools that are uncommon across the fleet.

```sqlite target=endpoint role=baseline params=(lookback_days=lookback_days, scope_hosts=scope_hosts, discovery_tools=discovery_tools)
~~~yaml
baseline:
  compare: first_seen
  window: '{{lookback_days}}d'
expected: Discovery tools used on a small number of hosts. Silence proves no rare
  usage of these specific binaries.
prevalence:
  by: device_hostname
  key:
  - tool_name
  rare_below: 5
reads:
- device_hostname
- process_name
- process_original_file_name
- time
silence: not_evidence_of_absence
source: hb_process_activity
verified: dry-run
verified_at: '2026-10-30'
~~~
SELECT LOWER(process_name) AS tool_name, COUNT(DISTINCT device_hostname) AS host_count, MIN(time) AS first_seen FROM hb_process_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (instr(',' || '{{discovery_tools}}' || ',', ',' || LOWER(process_name) || ',') > 0 OR instr(',' || '{{discovery_tools}}' || ',', ',' || LOWER(process_original_file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days') GROUP BY 1 HAVING host_count < 5 ORDER BY host_count ASC
```

## keychain-details
<!-- Search for macOS keychain scraping -->
Find scripts targeting AI platform secrets within the macOS keychain.

```sqlite target=endpoint role=triage params=(lookback_days=lookback_days, scope_hosts=scope_hosts)
~~~yaml
expected: Script blocks using native macOS commands to access the keychain for AI
  platform secrets. Silence indicates no such scripted scraping was detected.
reads:
- device_hostname
- actor_user_name
- script_content
- time
silence: not_evidence_of_absence
source: hb_script_activity
verified: dry-run
verified_at: '2026-10-30'
~~~
SELECT device_hostname, actor_user_name, script_content, time FROM hb_script_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (LOWER(script_content) LIKE '%security find-generic-password%' OR LOWER(script_content) LIKE '%security find-internet-password%') AND (LOWER(script_content) LIKE '%anthropic%' OR LOWER(script_content) LIKE '%claude%') AND time >= datetime('now', '-{{lookback_days}} days')
```

## early-triage
<!-- Assess early discovery and theft -->
```agent target=hunter
cite: required
context:
- scoping-ai-tools
- discovery-prevalence
- keychain-details
max_iterations: 4
objective: Identify hosts that have targeted AI software and exhibit rare discovery
  tool usage or keychain scraping activity.
success_criteria: A per-host verdict citing specific rows from the inventory and script
  logs.
tools:
- endpoint
```

## parallel-follow-on
<!-- Examine exfiltration and ransom -->
parallel:
- → exfiltration-rclone
- → ransom-impact
join: → final-triage

## exfiltration-rclone
<!-- Detect Rclone exfiltration activity -->
Identify the use of Rclone for exfiltrating data, as observed in VYPR campaigns.

```sqlite target=endpoint role=baseline params=(lookback_days=lookback_days, scope_hosts=scope_hosts, rclone_keywords=rclone_keywords)
~~~yaml
baseline:
  compare: first_seen
  window: '{{lookback_days}}d'
expected: Rclone executions that are rare across the estate, specifically those involving
  sync or copy operations. Silence indicates no unusual Rclone usage was seen.
prevalence:
  by: device_hostname
  key:
  - tool_name
  rare_below: 5
reads:
- device_hostname
- process_name
- process_original_file_name
- process_cmd_line
- user_name
- time
silence: not_evidence_of_absence
source: hb_process_activity
verified: dry-run
verified_at: '2026-10-30'
~~~
SELECT LOWER(process_name) AS tool_name, device_hostname, process_cmd_line, COUNT(DISTINCT device_hostname) AS host_count, MIN(time) AS first_seen FROM hb_process_activity WHERE (('{{scope_hosts}}' = '' OR instr(',' || '{{scope_hosts}}' || ',', ',' || device_hostname || ',') > 0)) AND (instr(',' || '{{rclone_keywords}}' || ',', ',' || LOWER(process_name) || ',') > 0 OR instr(',' || '{{rclone_keywords}}' || ',', ',' || LOWER(process_original_file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days') GROUP BY 1, 2, 3 HAVING host_count < 5 ORDER BY host_count ASC
```

## ransom-impact
<!-- Identify ransom note creation -->
Find definitive evidence of ransomware deployment from VYPR or Interlock variants.

```sqlite target=endpoint role=detection-candidate params=(lookback_days=lookback_days, ransom_notes=ransom_notes)
~~~yaml
expected: The query detects the creation of known ransom note filenames on disk. Silence
  proves no known note filenames were created in the window.
reads:
- device_hostname
- file_name
- file_path
- process_name
- time
silence: not_evidence_of_absence
source: hb_file_activity
verified: dry-run
verified_at: '2026-10-30'
~~~
SELECT device_hostname, file_name, file_path, process_name, time FROM hb_file_activity WHERE activity_id = 1 AND (instr(',' || '{{ransom_notes}}' || ',', ',' || LOWER(file_name) || ',') > 0) AND time >= datetime('now', '-{{lookback_days}} days')
```

## final-triage
<!-- Synthesize intrusion chain -->
```agent target=hunter
cite: required
context:
- early-triage
- exfiltration-rclone
- ransom-impact
max_iterations: 6
objective: Correlate the early-stage triage results with follow-on exfiltration and
  ransom note findings to confirm a high-confidence ransomware intrusion.
success_criteria: A comprehensive verdict citing the transition from secret theft
  to final ransomware impact.
tools:
- endpoint
```

## route-verdict
<!-- Route on final verdict -->
if~: "the final triage verdict is malicious for at least one host, particularly where ransom notes or Rclone exfiltration is confirmed" (confidence: high, judge=hunter)
then: → isolate-host
indeterminate: → analyst-remediation
unavailable: → analyst-remediation (blind_spot: no-agent-coverage)
else: → analyst-remediation

## isolate-host
<!-- Isolate impacted endpoint -->
```action target=endpoint
~~~yaml
approval: required
~~~
Isolate the host via the EDR console and revoke any active sessions for users logged into the system.
```
→ analyst-remediation

## analyst-remediation
<!-- Perform manual remediation -->
```manual target=analyst
Rotate all AI platform API keys found on the host. Review process logs for the source of Rclone and PsExec. Document any confirmed data theft.
```
→ close-out

## close-out
<!-- Close out hunt -->
```manual target=analyst
Record the hosts examined and any confirmed IOCs. Promote the ransom note query to a permanent detection rule.
```
→ end

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Machine-drafted by huntbase-hunt-generation using hb_google/gemini-3-flash-preview, gated by dry-run, lint, then reviewed by a person.

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