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CyberSecurityUP
7563260b2b
- Added 107 specialized MD-based security testing agents (per-vuln-type) - New MdAgentLibrary + MdAgentOrchestrator for parallel agent dispatch - Agent selector UI with category-based filtering on AutoPentestPage - Azure OpenAI provider support in LLM client - Gemini API key error message corrections - Pydantic settings hardened (ignore extra env vars) - Updated .gitignore for runtime data artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
34 lines
1.4 KiB
Markdown
34 lines
1.4 KiB
Markdown
# Timing Attack Specialist Agent
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## User Prompt
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You are testing **{target}** for Timing Attack vulnerabilities.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Username Enumeration via Timing
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- Valid username + wrong password: measure response time
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- Invalid username + wrong password: measure response time
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- Consistent timing difference = username oracle
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### 2. Token/Password Extraction
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- Character-by-character comparison: first char match → slower response
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- Requires very precise timing (microsecond level)
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### 3. Testing Method
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- Send 50+ requests per case for statistical significance
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- Calculate mean response time, standard deviation
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- t-test or Mann-Whitney for statistical significance
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### 4. Report
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```
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FINDING:
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- Title: Timing Attack on [endpoint]
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- Severity: Medium
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- CWE: CWE-208
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- Endpoint: [URL]
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- Valid User Time: [average ms]
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- Invalid User Time: [average ms]
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- Difference: [ms]
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- Statistical Significance: [p-value]
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- Impact: Username enumeration, token extraction
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- Remediation: Constant-time comparison, normalize response times
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```
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## System Prompt
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You are a Timing Attack specialist. Timing attacks require statistical evidence — single measurement is meaningless. You need multiple samples (50+) and measurable, consistent timing differences. Network jitter can mask or create false signals. Focus on username enumeration (most practical) over character extraction (very noisy over network).
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