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Security Operations🟡 Intermediate

Hunting Credential Stuffing Attacks

Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins. Uses statistical analysis on Splunk or raw log data.

3 min read2 code examples

Hunting Credential Stuffing Attacks

Instructions

Analyze authentication logs to detect credential stuffing by identifying patterns

of distributed login failures, high IP diversity, and suspicious ASN distribution.

import pandas as pd
from collections import Counter

# Load auth logs
df = pd.read_csv("auth_logs.csv", parse_dates=["timestamp"])

# Credential stuffing indicator: many IPs trying few accounts
ip_per_account = df[df["status"] == "failed"].groupby("username")["source_ip"].nunique()
accounts_under_attack = ip_per_account[ip_per_account > 50]

Key detection indicators:

  1. High unique source IPs per failed username
  2. Low success rate across many accounts (< 1%)
  3. ASN concentration from cloud/proxy providers
  4. Geographic impossibility (same account, distant locations)
  5. User-agent uniformity across distributed IPs

Examples

# Password spray: one password tried across many accounts
spray = df[df["status"] == "failed"].groupby(["source_ip", "password_hash"]).agg(
    accounts=("username", "nunique")).reset_index()
sprays = spray[spray["accounts"] > 10]

Verification Criteria

Confirm successful execution by validating:

  • [ ] All prerequisite tools and access requirements are satisfied
  • [ ] Each workflow step completed without errors
  • [ ] Output matches expected format and contains expected data
  • [ ] No security warnings or misconfigurations detected
  • [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

  • SOC 2: CC7.1 (Monitoring), CC7.2 (Anomaly Detection), CC7.3 (Incident Identification)
  • ISO 27001: A.12.4 (Logging & Monitoring), A.16.1 (Security Incident Management)
  • NIST 800-53: AU-6 (Audit Review), SI-4 (System Monitoring), IR-5 (Incident Monitoring)
  • NIST CSF: DE.AE (Anomalies & Events), DE.CM (Continuous Monitoring)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add hunting-credential-stuffing-attacks

# Or load dynamically via MCP
grc.load_skill("hunting-credential-stuffing-attacks")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

  • SHA-256 chain hashing ensures no step can be modified after execution
  • Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
  • Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.

Use with Claw GRC Agents

This skill is fully compatible with Claw GRC's autonomous agent system. Deploy it to any registered agent via MCP, and every execution will be logged in the tamper-evident audit trail.

// Load this skill in your agent
npx claw-grc skills add hunting-credential-stuffing-attacks
// Or via MCP
grc.load_skill("hunting-credential-stuffing-attacks")

Tags

huntingcredentialstuffingattacks

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Skill Details

Domain
Security Operations
Difficulty
intermediate
Read Time
3 min
Code Examples
2

On This Page

InstructionsExamplesVerification CriteriaCompliance Framework MappingDeploying This Skill with Claw GRC

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