Threat Hunting Exercise: Analyzing IDS SQLMap Automated Injection Signature Dataset

This hunt uses a simulated network IDS and web application log dataset (ids_sqlmap_signature_2022-10-29T144500.json) capturing T1190: Exploit Public-Facing Application (SQL Injection), where the IDS flags the distinctive User-Agent and payload structure of the automated sqlmap tool, and the hunt determines whether the automated injection attempts succeeded in extracting data from the backend database.

Step 1: Hypothesis Formation

Hypothesis: An IDS signature fires on the characteristic sqlmap User-Agent string or boolean/time-based blind-injection payload patterns (AND 1=1, SLEEP(5), UNION SELECT) against a web application parameter, and correlating the corresponding web application response times/sizes with the injection type used reveals whether the backend database actually returned attacker-influenced data - distinguishing a successful, data-extracting attack from a blocked or failed injection attempt. Indicators:

• IDS signature match for sqlmap default User-Agent (sqlmap/1.x) or characteristic payload syntax across many requests to the same parameter in rapid succession (automated tool cadence).
• Web application response times correlating precisely with injected SLEEP()/WAITFOR DELAY values (confirms time-based blind SQLi is functioning, i.e., the query is executing against the database).
• HTTP response body size anomalies correlating with boolean-based (1=1 vs 1=2) payload variations (confirms boolean-based blind SQLi is functioning).
• Any response containing data patterns inconsistent with the application's normal output (e.g., database version strings, table names) indicating successful UNION-based extraction.

Null Hypothesis: A security team's own authorized penetration test or vulnerability scan is running sqlmap against the application as part of a scheduled assessment. Invalidate by checking the source IP and timing against the approved security-testing calendar.

Rationale: sqlmap is loud, automated, and easily signatured, so IDS alerts are common and often triaged only for volume; correlating the payload type with the actual application response (timing/size) reveals which alerts represent a genuinely vulnerable, actively-exploited parameter versus a WAF-blocked or non-vulnerable attempt.

Step 2: Data Sources and Scope

• Sources: IDS/IPS alert logs; web application access logs with response time and size; WAF block/allow decisions.
• Scope: ~2022-10-29T14:45:00-15:20:00 UTC; Target: /search?category= parameter on shop.corp-example.com; Source: 91.208.184.55.
• SIEM Queries (Splunk/ELK):
• index=ids signature="ET WEB_SPECIFIC_APPS SQLMap" OR user_agent="sqlmap"
• Time-based correlation: index=web_access uri="/search*" src_ip=91.208.184.55 | eval delay_injected=if(match(query,"SLEEP\(\d+\)"),1,0) | stats avg(response_time) by delay_injected
• Boolean-based correlation: index=web_access uri="/search*" src_ip=91.208.184.55 | stats avg(response_size) by query_boolean_value

Step 3: Key Findings

Parsed events (5 shown) confirm sqlmap was used against the /search?category= parameter, and response-timing correlation confirms the time-based blind injection technique successfully executed against the backend database, indicating genuine data-extraction risk rather than a blocked attempt.

Validation:

• Timeline: automated tool signature detected, time-based blind injection confirmed via precise response-time correlation, followed by successful UNION-based schema extraction revealing sensitive table names - a complete and confirmed successful SQL injection attack.
• False Positives: no approved testing engagement or IP-range match exists, ruling out authorized security assessment activity.
• Correlation: tool signature, timing-based confirmation, and direct schema-data leakage in the response body jointly confirm a successful, actively-exploited SQL injection vulnerability with real data-extraction risk.

Step 4: Recommendations & Next Steps

• Immediate Response: Block 91.208.184.55 at the perimeter/WAF immediately, apply emergency input-validation/parameterized-query remediation to the /search endpoint, review database audit logs for the full scope of tables/records accessed via the confirmed UNION extraction, and notify the data-protection/legal team given evidence of customers and payment_methods table exposure.
• Detection: Sigma-style rule: title: Confirmed SQL Injection via Time-Based Response Correlation → selection: uri contains 'SLEEP(' OR uri contains 'WAITFOR DELAY' correlation: response_time within 1s of injected delay value → condition: selection and correlation.
• Pro Tip: Don't stop at the IDS signature alone - build a lightweight automated correlation that compares response time/size against the specific SQLi payload type (time-based vs boolean-based) for every alert; this turns a generic "sqlmap was seen" alert into a confirmed "the database actually responded to attacker-controlled logic" finding, which is the difference between a low-priority triage item and an emergency data-breach response.

Hypothesis confirmed - automated sqlmap scanning against the /search parameter succeeded in both confirming a working time-based blind SQL injection and extracting sensitive database table names including customers and payment_methods via UNION-based injection!