⚡ v7.0.0 Stable • Empirical Latency Calibration • Content-Diff Normalization

High-Precision Blind SQL Injection
Testing & Verification Framework

Empirically calibrated Time-Based and Boolean-Based blind SQL injection testing. Calibrates baseline latency jitter (μ + 3.5σ) and strips dynamic noise to guarantee verifiable findings.

$ git clone https://github.com/pratik-khairnar-sec/BlindStrike.git

Baseline Calibration & Anomaly Simulator

Visualize the statistical thresholding and retest mechanism used by BlindStrike

Time-Based Calibration Parameters μ + 3.5σ Threshold
Target Sleep Delay 5.0s
Network Jitter Variance (σ) ± 25ms
Simulated Latency Samples 5 Baseline + 1 Probe + 1 Retest
124 ms
Baseline Mean (μ)
18 ms
Variance (σ)
5,187 ms
Anomaly Threshold
🎯 CONFIRMED VULNERABLE — Retest Validated (+5,024ms)
[0.000s] Target: https://api.target.local/products?cat=1 [0.015s] Phase 1: Calibrating baseline network latency (5 samples)... [0.132s] Sample 1: 122ms | Sample 2: 129ms | Sample 3: 118ms | Sample 4: 135ms | Sample 5: 121ms [0.133s] Calibration Computed: Mean μ = 125.0ms, StdDev σ = 6.4ms [0.134s] Dynamic Threshold: 5,147ms (Delay: 5000ms + Noise Floor) [0.135s] Phase 2: Injecting payload with 5s delay... [5.285s] Response received in 5,150ms [EXCEEDS THRESHOLD] [5.286s] Phase 3: Automated Confirmation Retest dispatched... [10.435s] Retest Response received in 5,149ms [CONFIRMED] [10.436s] Verdict: HIGH-CONFIDENCE TIME-BASED SQL INJECTION CONFIRMED.

Precision Detection Capabilities

Engineered specifically to solve the high-jitter problem in Blind SQLi

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Empirical Latency Calibration

Samples baseline round-trip latencies dynamically (μ + 3.5σ) before testing, establishing a mathematically sound noise floor that resists erratic Wi-Fi and cloud jitter.

⚖️

Content-Diff Normalization

Boolean engine strips transient session tokens, nonces, CSRF parameters, and timestamps to calculate true DOM tree divergence between TRUE and FALSE queries.

🔁

Automated Confirmation Retests

Any candidate timing anomaly immediately triggers an independent verification retest with identical or randomized delay to eliminate one-off network spikes.

🎯

Precise FUZZ Injection

Inject seamlessly into query parameters, RESTful URL paths, custom request headers (Authorization, User-Agent), or nested JSON POST bodies.

🤖

Live Telegram Alerts

Instant push notifications with exact reproducible curl commands, injected payload details, and automatically attached HTML reports.

📊

Dark Glassmorphic Reports

Generates client-ready HTML reports featuring 1-click reproduction curl commands, parameter tables, and timing breakdown charts.

Configure Your Test

Generate tailored BlindStrike execution commands

blindstrike -u "https://target.com/api/items?id=101FUZZ" --mode time --delay 5 --stop-on-confirm

BlindStrike vs Alternative Approaches

Solving the latency jitter dilemma once and for all

Testing Feature BlindStrike v7.0 Standard sqlmap Manual Sleep Payloads
Empirical Latency Calibration ✔ Dynamic μ + 3.5σ calibration ✖ Fixed heuristic threshold ✖ Manual estimation
Automated Retest Confirmation ✔ Automatic verification retest ✔ Optional retest ✖ Manual repeat
Dynamic Noise Token Stripping ✔ Strips CSRF/session tokens ✔ Ratio comparison ✖ None
Speed & Lightweight Footprint ✔ Ultra-fast async Python CLI ✖ Heavy DBMS fingerprinting ✔ Single requests
Instant Mobile Notifications ✔ Direct Telegram Bot integration ✖ None ✖ None