Update inference.py #5953

kn1g78 · 2025-08-31T17:15:29Z

SQLMap Dictionary Similarity Feature

Feature Description

This feature adds intelligent dictionary matching capability to sqlmap's blind injection. When the partially inferred string during blind injection has high similarity with common database/table names in the dictionary, it will directly test the complete dictionary item, greatly saving blind injection time.

How It Works

Similarity Check: During blind injection, when at least 3 characters are inferred, it checks similarity with dictionary items
Threshold Judgment: If similarity exceeds 0.8 (configurable), it attempts to test the complete dictionary item
Fast Matching: If the test succeeds, it directly returns the complete result, skipping remaining character inference

Usage

1. Dictionary Files

The feature automatically selects the appropriate dictionary based on the query type:

Tables/Databases: Uses data/txt/common-tables.txt for table and database name inference
Columns: Uses data/txt/common-columns.txt for column name inference

These files already contain common database, table, and column names. You can add more entries to these files if needed.

2. Normal sqlmap Usage

The feature is automatically enabled without additional parameters. When blind injection encounters content similar to dictionary items, it will display information like:

[INFO] checking tables dictionary similarity for 'info' (similarity: 0.90)
[INFO] Tables dictionary match successful: 'information_schema'
[INFO] checking columns dictionary similarity for 'user' (similarity: 0.85)
[INFO] Columns dictionary match successful: 'username'

Configuration Options

Similarity Threshold

You can modify the similarity threshold in lib/techniques/blind/inference.py:

# Similarity threshold for dictionary matching
SIMILARITY_THRESHOLD = 0.8  # Can be adjusted between 0.7-0.9

Minimum Character Count

By default, similarity check is only performed when at least 3 characters are inferred. You can modify this in the code:

if len(partialValue) >= 3:  # Can be adjusted between 2-5

Advantages

Significant Time Savings: For common database/table names, can save over 90% of blind injection time
Intelligent Matching: Uses sequence matching algorithm to handle partial and similar matches
Prefix Priority: Gives higher weight to prefix matches, improving accuracy
Backward Compatibility: Does not affect existing blind injection functionality, only adds optimization

Example Scenarios

Scenario 1: Inferring Database Name

Blind injection inference: info -> information_schema
Time saved: Reduced from 15 character inferences to 4 character inferences

Scenario 2: Inferring Table Name

Blind injection inference: user -> users
Time saved: Reduced from 5 character inferences to 4 character inferences

Scenario 3: Inferring Admin Table

Blind injection inference: adm -> admin
Time saved: Reduced from 5 character inferences to 3 character inferences

Scenario 4: Inferring Column Names

Blind injection inference: user -> username
Time saved: Reduced from 8 character inferences to 4 character inferences

Notes

Dictionary Quality: The quality of the dictionary file directly affects matching effectiveness
False Matches: If the dictionary contains irrelevant items, it may cause false matches
Performance Impact: Similarity checking brings minimal performance overhead, but the time saved is worth it

Troubleshooting

Dictionary File Not Found

Ensure data/txt/common-tables.txt and data/txt/common-columns.txt files exist and are readable

Similarity Check Not Working

Check if there are "checking dictionary similarity" messages in the logs

Match Failure

Try lowering the similarity threshold or adding more relevant items to the appropriate dictionary file

Technical Implementation

Similarity Algorithm: Uses Python's difflib.SequenceMatcher
Integration Location: lib/techniques/blind/inference.py
Supported Modes: Single-threaded and multi-threaded blind injection
Caching Mechanism: Dictionary content is cached for performance

stamparm · 2025-08-31T17:21:57Z

https://github.com/sqlmapproject/sqlmap/wiki/usage#output-prediction

…

On Sun, Aug 31, 2025, 19:15 ***@***.*** ***@***.***> wrote: SQLMap Dictionary Similarity Feature Feature Description This feature adds intelligent dictionary matching capability to sqlmap's blind injection. When the partially inferred string during blind injection has high similarity with common database/table names in the dictionary, it will directly test the complete dictionary item, greatly saving blind injection time. How It Works 1. *Similarity Check*: During blind injection, when at least 3 characters are inferred, it checks similarity with dictionary items 2. *Threshold Judgment*: If similarity exceeds 0.8 (configurable), it attempts to test the complete dictionary item 3. *Fast Matching*: If the test succeeds, it directly returns the complete result, skipping remaining character inference Usage 1. Dictionary Files The feature automatically selects the appropriate dictionary based on the query type: - *Tables/Databases*: Uses data/txt/common-tables.txt for table and database name inference - *Columns*: Uses data/txt/common-columns.txt for column name inference These files already contain common database, table, and column names. You can add more entries to these files if needed. 2. Normal sqlmap Usage The feature is automatically enabled without additional parameters. When blind injection encounters content similar to dictionary items, it will display information like: [INFO] checking tables dictionary similarity for 'info' (similarity: 0.90) [INFO] Tables dictionary match successful: 'information_schema' [INFO] checking columns dictionary similarity for 'user' (similarity: 0.85) [INFO] Columns dictionary match successful: 'username' Configuration Options Similarity Threshold You can modify the similarity threshold in lib/techniques/blind/inference.py: # Similarity threshold for dictionary matchingSIMILARITY_THRESHOLD = 0.8 # Can be adjusted between 0.7-0.9 Minimum Character Count By default, similarity check is only performed when at least 3 characters are inferred. You can modify this in the code: if len(partialValue) >= 3: # Can be adjusted between 2-5 Advantages 1. *Significant Time Savings*: For common database/table names, can save over 90% of blind injection time 2. *Intelligent Matching*: Uses sequence matching algorithm to handle partial and similar matches 3. *Prefix Priority*: Gives higher weight to prefix matches, improving accuracy 4. *Backward Compatibility*: Does not affect existing blind injection functionality, only adds optimization Example Scenarios Scenario 1: Inferring Database Name - Blind injection inference: info -> information_schema - Time saved: Reduced from 15 character inferences to 4 character inferences Scenario 2: Inferring Table Name - Blind injection inference: user -> users - Time saved: Reduced from 5 character inferences to 4 character inferences Scenario 3: Inferring Admin Table - Blind injection inference: adm -> admin - Time saved: Reduced from 5 character inferences to 3 character inferences Scenario 4: Inferring Column Names - Blind injection inference: user -> username - Time saved: Reduced from 8 character inferences to 4 character inferences Notes 1. *Dictionary Quality*: The quality of the dictionary file directly affects matching effectiveness 2. *False Matches*: If the dictionary contains irrelevant items, it may cause false matches 3. *Performance Impact*: Similarity checking brings minimal performance overhead, but the time saved is worth it Troubleshooting Dictionary File Not Found Ensure data/txt/common-tables.txt and data/txt/common-columns.txt files exist and are readable Similarity Check Not Working Check if there are "checking dictionary similarity" messages in the logs Match Failure Try lowering the similarity threshold or adding more relevant items to the appropriate dictionary file Technical Implementation - *Similarity Algorithm*: Uses Python's difflib.SequenceMatcher - *Integration Location*: lib/techniques/blind/inference.py - *Supported Modes*: Single-threaded and multi-threaded blind injection - *Caching Mechanism*: Dictionary content is cached for performance ------------------------------ You can view, comment on, or merge this pull request online at: #5953 Commit Summary - bb1120d <bb1120d> Update inference.py File Changes (1 file <https://github.com/sqlmapproject/sqlmap/pull/5953/files>) - *M* lib/techniques/blind/inference.py <https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1> (193) Patch Links: - https://github.com/sqlmapproject/sqlmap/pull/5953.patch - https://github.com/sqlmapproject/sqlmap/pull/5953.diff — Reply to this email directly, view it on GitHub <#5953>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

kn1g78 · 2025-09-01T11:56:31Z

https://github.com/sqlmapproject/sqlmap/wiki/usage#output-prediction
…
On Sun, Aug 31, 2025, 19:15 @.*** @.> wrote: SQLMap Dictionary Similarity Feature Feature Description This feature adds intelligent dictionary matching capability to sqlmap's blind injection. When the partially inferred string during blind injection has high similarity with common database/table names in the dictionary, it will directly test the complete dictionary item, greatly saving blind injection time. How It Works 1. Similarity Check: During blind injection, when at least 3 characters are inferred, it checks similarity with dictionary items 2. Threshold Judgment: If similarity exceeds 0.8 (configurable), it attempts to test the complete dictionary item 3. Fast Matching: If the test succeeds, it directly returns the complete result, skipping remaining character inference Usage 1. Dictionary Files The feature automatically selects the appropriate dictionary based on the query type: - Tables/Databases: Uses data/txt/common-tables.txt for table and database name inference - Columns: Uses data/txt/common-columns.txt for column name inference These files already contain common database, table, and column names. You can add more entries to these files if needed. 2. Normal sqlmap Usage The feature is automatically enabled without additional parameters. When blind injection encounters content similar to dictionary items, it will display information like: [INFO] checking tables dictionary similarity for 'info' (similarity: 0.90) [INFO] Tables dictionary match successful: 'information_schema' [INFO] checking columns dictionary similarity for 'user' (similarity: 0.85) [INFO] Columns dictionary match successful: 'username' Configuration Options Similarity Threshold You can modify the similarity threshold in lib/techniques/blind/inference.py: # Similarity threshold for dictionary matchingSIMILARITY_THRESHOLD = 0.8 # Can be adjusted between 0.7-0.9 Minimum Character Count By default, similarity check is only performed when at least 3 characters are inferred. You can modify this in the code: if len(partialValue) >= 3: # Can be adjusted between 2-5 Advantages 1. Significant Time Savings: For common database/table names, can save over 90% of blind injection time 2. Intelligent Matching: Uses sequence matching algorithm to handle partial and similar matches 3. Prefix Priority: Gives higher weight to prefix matches, improving accuracy 4. Backward Compatibility: Does not affect existing blind injection functionality, only adds optimization Example Scenarios Scenario 1: Inferring Database Name - Blind injection inference: info -> information_schema - Time saved: Reduced from 15 character inferences to 4 character inferences Scenario 2: Inferring Table Name - Blind injection inference: user -> users - Time saved: Reduced from 5 character inferences to 4 character inferences Scenario 3: Inferring Admin Table - Blind injection inference: adm -> admin - Time saved: Reduced from 5 character inferences to 3 character inferences Scenario 4: Inferring Column Names - Blind injection inference: user -> username - Time saved: Reduced from 8 character inferences to 4 character inferences Notes 1. Dictionary Quality: The quality of the dictionary file directly affects matching effectiveness 2. False Matches: If the dictionary contains irrelevant items, it may cause false matches 3. Performance Impact: Similarity checking brings minimal performance overhead, but the time saved is worth it Troubleshooting Dictionary File Not Found Ensure data/txt/common-tables.txt and data/txt/common-columns.txt files exist and are readable Similarity Check Not Working Check if there are "checking dictionary similarity" messages in the logs Match Failure Try lowering the similarity threshold or adding more relevant items to the appropriate dictionary file Technical Implementation - Similarity Algorithm: Uses Python's difflib.SequenceMatcher - Integration Location: lib/techniques/blind/inference.py - Supported Modes: Single-threaded and multi-threaded blind injection - Caching Mechanism: Dictionary content is cached for performance ------------------------------ You can view, comment on, or merge this pull request online at: #5953 Commit Summary - bb1120d <bb1120d> Update inference.py File Changes (1 file https://github.com/sqlmapproject/sqlmap/pull/5953/files) - M lib/techniques/blind/inference.py https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1 (193) Patch Links: - https://github.com/sqlmapproject/sqlmap/pull/5953.patch - https://github.com/sqlmapproject/sqlmap/pull/5953.diff — Reply to this email directly, view it on GitHub <#5953>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI . You are receiving this because you are subscribed to this thread.Message ID: @.>

The --predict-output parameter allows only 1 thread, and there is no similarity match, only prefix matching.

stamparm · 2025-09-01T12:04:42Z

i am not really convinced in this all. based on 3 characters if there is 0.8 similarity you do a guess. this is prone to fubar due to loads of cache misses. i dont see this usable in real world scenarios. you will just send tons of equal check requests provoking tons of misses. like, if there is "use" you will just send "user" in hope that there is a user at the other end

…

On Mon, Sep 1, 2025, 13:56 ***@***.*** ***@***.***> wrote: *kn1g78* left a comment (sqlmapproject/sqlmap#5953) <#5953 (comment)> https://github.com/sqlmapproject/sqlmap/wiki/usage#output-prediction … <#m_-3157782063329569616_> On Sun, Aug 31, 2025, 19:15 *@*.*** *@*.*> wrote: SQLMap Dictionary Similarity Feature Feature Description This feature adds intelligent dictionary matching capability to sqlmap's blind injection. When the partially inferred string during blind injection has high similarity with common database/table names in the dictionary, it will directly test the complete dictionary item, greatly saving blind injection time. How It Works 1. Similarity Check: During blind injection, when at least 3 characters are inferred, it checks similarity with dictionary items 2. Threshold Judgment: If similarity exceeds 0.8 (configurable), it attempts to test the complete dictionary item 3. Fast Matching: If the test succeeds, it directly returns the complete result, skipping remaining character inference Usage 1. Dictionary Files The feature automatically selects the appropriate dictionary based on the query type: - Tables/Databases: Uses data/txt/common-tables.txt for table and database name inference - Columns: Uses data/txt/common-columns.txt for column name inference These files already contain common database, table, and column names. You can add more entries to these files if needed. 2. Normal sqlmap Usage The feature is automatically enabled without additional parameters. When blind injection encounters content similar to dictionary items, it will display information like: [INFO] checking tables dictionary similarity for 'info' (similarity: 0.90) [INFO] Tables dictionary match successful: 'information_schema' [INFO] checking columns dictionary similarity for 'user' (similarity: 0.85) [INFO] Columns dictionary match successful: 'username' Configuration Options Similarity Threshold You can modify the similarity threshold in lib/techniques/blind/inference.py: # Similarity threshold for dictionary matchingSIMILARITY_THRESHOLD = 0.8 # Can be adjusted between 0.7-0.9 Minimum Character Count By default, similarity check is only performed when at least 3 characters are inferred. You can modify this in the code: if len(partialValue) >= 3: # Can be adjusted between 2-5 Advantages 1. Significant Time Savings: For common database/table names, can save over 90% of blind injection time 2. Intelligent Matching: Uses sequence matching algorithm to handle partial and similar matches 3. Prefix Priority: Gives higher weight to prefix matches, improving accuracy 4. Backward Compatibility: Does not affect existing blind injection functionality, only adds optimization Example Scenarios Scenario 1: Inferring Database Name - Blind injection inference: info -> information_schema - Time saved: Reduced from 15 character inferences to 4 character inferences Scenario 2: Inferring Table Name - Blind injection inference: user -> users - Time saved: Reduced from 5 character inferences to 4 character inferences Scenario 3: Inferring Admin Table - Blind injection inference: adm -> admin - Time saved: Reduced from 5 character inferences to 3 character inferences Scenario 4: Inferring Column Names - Blind injection inference: user -> username - Time saved: Reduced from 8 character inferences to 4 character inferences Notes 1. Dictionary Quality: The quality of the dictionary file directly affects matching effectiveness 2. False Matches: If the dictionary contains irrelevant items, it may cause false matches 3. Performance Impact: Similarity checking brings minimal performance overhead, but the time saved is worth it Troubleshooting Dictionary File Not Found Ensure data/txt/common-tables.txt and data/txt/common-columns.txt files exist and are readable Similarity Check Not Working Check if there are "checking dictionary similarity" messages in the logs Match Failure Try lowering the similarity threshold or adding more relevant items to the appropriate dictionary file Technical Implementation - Similarity Algorithm: Uses Python's difflib.SequenceMatcher - Integration Location: lib/techniques/blind/inference.py - Supported Modes: Single-threaded and multi-threaded blind injection - Caching Mechanism: Dictionary content is cached for performance ------------------------------ You can view, comment on, or merge this pull request online at: #5953 <#5953> Commit Summary - bb1120d <bb1120d> <bb1120d <bb1120d>> Update inference.py File Changes (1 file https://github.com/sqlmapproject/sqlmap/pull/5953/files <https://github.com/sqlmapproject/sqlmap/pull/5953/files>) - M lib/techniques/blind/inference.py https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1 <https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1> (193) Patch Links: - https://github.com/sqlmapproject/sqlmap/pull/5953.patch <https://github.com/sqlmapproject/sqlmap/pull/5953.patch> - https://github.com/sqlmapproject/sqlmap/pull/5953.diff <https://github.com/sqlmapproject/sqlmap/pull/5953.diff> — Reply to this email directly, view it on GitHub <#5953 <#5953>>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI <https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI> . You are receiving this because you are subscribed to this thread.Message ID: @.*> The --predict-output parameter allows only 1 thread, and there is no similarity match, only prefix matching. — Reply to this email directly, view it on GitHub <#5953 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAHA7U2YL3TWDRND57LQ23D3QQYALAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTENBSGA4DKNBTHA> . You are receiving this because you commented.Message ID: ***@***.***>

kn1g78 · 2025-09-01T12:12:37Z

我对这一切并不完全相信。基于 3 个字符（如果有） 0.8 相似度你猜测。由于缓存负载，这很容易出现 fubar 错过。我认为这在现实场景中不可用。您只需发送大量的等额检查请求引发了大量的失误。比如，如果有的话 “使用”你只会发送“用户”，希望另一个人有一个用户结束
…
On Mon, Sep 1, 2025, 13:56 @.*** @.> wrote: kn1g78 left a comment (sqlmapproject/sqlmap#5953) <#5953 (comment)> https://github.com/sqlmapproject/sqlmap/wiki/usage#output-prediction … <#m_-3157782063329569616_> On Sun, Aug 31, 2025, 19:15 @. @.> wrote: SQLMap Dictionary Similarity Feature Feature Description This feature adds intelligent dictionary matching capability to sqlmap's blind injection. When the partially inferred string during blind injection has high similarity with common database/table names in the dictionary, it will directly test the complete dictionary item, greatly saving blind injection time. How It Works 1. Similarity Check: During blind injection, when at least 3 characters are inferred, it checks similarity with dictionary items 2. Threshold Judgment: If similarity exceeds 0.8 (configurable), it attempts to test the complete dictionary item 3. Fast Matching: If the test succeeds, it directly returns the complete result, skipping remaining character inference Usage 1. Dictionary Files The feature automatically selects the appropriate dictionary based on the query type: - Tables/Databases: Uses data/txt/common-tables.txt for table and database name inference - Columns: Uses data/txt/common-columns.txt for column name inference These files already contain common database, table, and column names. You can add more entries to these files if needed. 2. Normal sqlmap Usage The feature is automatically enabled without additional parameters. When blind injection encounters content similar to dictionary items, it will display information like: [INFO] checking tables dictionary similarity for 'info' (similarity: 0.90) [INFO] Tables dictionary match successful: 'information_schema' [INFO] checking columns dictionary similarity for 'user' (similarity: 0.85) [INFO] Columns dictionary match successful: 'username' Configuration Options Similarity Threshold You can modify the similarity threshold in lib/techniques/blind/inference.py: # Similarity threshold for dictionary matchingSIMILARITY_THRESHOLD = 0.8 # Can be adjusted between 0.7-0.9 Minimum Character Count By default, similarity check is only performed when at least 3 characters are inferred. You can modify this in the code: if len(partialValue) >= 3: # Can be adjusted between 2-5 Advantages 1. Significant Time Savings: For common database/table names, can save over 90% of blind injection time 2. Intelligent Matching: Uses sequence matching algorithm to handle partial and similar matches 3. Prefix Priority: Gives higher weight to prefix matches, improving accuracy 4. Backward Compatibility: Does not affect existing blind injection functionality, only adds optimization Example Scenarios Scenario 1: Inferring Database Name - Blind injection inference: info -> information_schema - Time saved: Reduced from 15 character inferences to 4 character inferences Scenario 2: Inferring Table Name - Blind injection inference: user -> users - Time saved: Reduced from 5 character inferences to 4 character inferences Scenario 3: Inferring Admin Table - Blind injection inference: adm -> admin - Time saved: Reduced from 5 character inferences to 3 character inferences Scenario 4: Inferring Column Names - Blind injection inference: user -> username - Time saved: Reduced from 8 character inferences to 4 character inferences Notes 1. Dictionary Quality: The quality of the dictionary file directly affects matching effectiveness 2. False Matches: If the dictionary contains irrelevant items, it may cause false matches 3. Performance Impact: Similarity checking brings minimal performance overhead, but the time saved is worth it Troubleshooting Dictionary File Not Found Ensure data/txt/common-tables.txt and data/txt/common-columns.txt files exist and are readable Similarity Check Not Working Check if there are "checking dictionary similarity" messages in the logs Match Failure Try lowering the similarity threshold or adding more relevant items to the appropriate dictionary file Technical Implementation - Similarity Algorithm: Uses Python's difflib.SequenceMatcher - Integration Location: lib/techniques/blind/inference.py - Supported Modes: Single-threaded and multi-threaded blind injection - Caching Mechanism: Dictionary content is cached for performance ------------------------------ You can view, comment on, or merge this pull request online at: #5953 <#5953> Commit Summary - bb1120d <bb1120d> <bb1120d <bb1120d>> Update inference.py File Changes (1 file https://github.com/sqlmapproject/sqlmap/pull/5953/files https://github.com/sqlmapproject/sqlmap/pull/5953/files) - M lib/techniques/blind/inference.py https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1 https://github.com/sqlmapproject/sqlmap/pull/5953/files#diff-3cb530f971e849ea54d25211720d753f4f8c769d58100cbd7c90f980b9ae87a1 (193) Patch Links: - https://github.com/sqlmapproject/sqlmap/pull/5953.patch https://github.com/sqlmapproject/sqlmap/pull/5953.patch - https://github.com/sqlmapproject/sqlmap/pull/5953.diff https://github.com/sqlmapproject/sqlmap/pull/5953.diff — Reply to this email directly, view it on GitHub <#5953 <#5953>>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI https://github.com/notifications/unsubscribe-auth/AAHA7U5EW2YNBL6WZXGYLRD3QMUUZAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43ASLTON2WKOZTGM3TANJQGY3TCNI . You are receiving this because you are subscribed to this thread.Message ID: @.> The --predict-output parameter allows only 1 thread, and there is no similarity match, only prefix matching. — Reply to this email directly, view it on GitHub <#5953 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAHA7U2YL3TWDRND57LQ23D3QQYALAVCNFSM6AAAAACFIOEK3WVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTENBSGA4DKNBTHA . You are receiving this because you commented.Message ID: @.***>

u r right.

Update inference.py

bb1120d

kn1g78 closed this Sep 1, 2025

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Update inference.py #5953

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Update inference.py #5953

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kn1g78 commented Aug 31, 2025

SQLMap Dictionary Similarity Feature

Feature Description

How It Works

Usage

1. Dictionary Files

2. Normal sqlmap Usage

Configuration Options

Similarity Threshold

Minimum Character Count

Advantages

Example Scenarios

Scenario 1: Inferring Database Name

Scenario 2: Inferring Table Name

Scenario 3: Inferring Admin Table

Scenario 4: Inferring Column Names

Notes

Troubleshooting

Dictionary File Not Found

Similarity Check Not Working

Match Failure

Technical Implementation

Uh oh!

stamparm commented Aug 31, 2025 via email

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kn1g78 commented Sep 1, 2025

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stamparm commented Sep 1, 2025 via email

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kn1g78 commented Sep 1, 2025

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