;`) should be labeled as False.
3. Function calls without string literals (`password=getPass();`) and environmental variable assignments (`password=${pass}`) should be labeled as False.
4. Base64 and other encoded data: the decision must be after research. Use True if original data contain are credentials.
5. Package and resource version hash is not a credential, so common hash string (`integrity sha512-W7s+uC5bikET2twEFg==`) is False.
6. Be careful about filetype when checking variable assignment:
In .yaml file row (`password=my_password`) can be a credential
but in .js or .py it cannot. This languages require quotations (' or ") for string declaration (`password="my_password"`).
7. Check if the file you are labeling is not a localization file. For example `config/locales/pt-BR.yml` is not a credentials, just a translation. So those should be labeled as False.
8. Any possible markers like "example.com" mean the credential is False for ML rules. And may be True for not-ML rules.
> We could see that many credentials exist in directories/files that have the same test purpose as test/tests.
> In the case of these values, people often judge that they contain a real credential, but we do not know whether this value is an actual usable credential or a value used only for testing purposes.
> We classify those values as True in order to prevent the case of missing real usable credentials.
> Since it may be necessary to separate the values in the future, we have separated the files for testing and the files that are not. (Check metadata or data set)
## Metadata
Metadata includes Ground Truth values and additional information for credential lines detected by various tools.
### Properties on the Metadata
| Name of property | Data Type | Description |
|--------------------|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
| Id | Integer | Credential ID |
| FileID | String | Filename hash. Used to download correct file from a external repo |
| Domain | String | Domain of repository. (ex. Github) |
| RepoName | String | Project name that credential was found |
| FilePath | String | File path where credential information was included |
| LineStart | Integer | Line start in file from 1, like in most editors. In common cases it equals LineEnd. |
| LineEnd | Integer | End line of credential MUST be great or equal like LineStart. Sort line_data_list with line_num for this. |
| GroundTruth | String | Ground Truth of this credential. True (T) / False (F,X) |
| ValueStart | Integer | Index of value on the line like in CredSweeper report. This is start position on LineStart. Empty or -1 means the markup for whole line (for False only) |
| ValueEnd | Integer | Index of character right after value ends in the line. This is end position on LineEnd. May be -1 or empty. |
| CryptographyKey | String | Type of a key: Private or Public |
| PredefinedPattern | String | Credential with defined regex patterns (AWS token with `AKIA...` pattern) |
| Category | String | Labeled data according CredSweeper rules. see [Category](#category). |
### Category
Labeled data for according rules with splitting by colon sign ``:``
Preferred sort in alphabetical order.
E.g. ``Slack Token:Token`` - the value is matched for 2 rules ``Token`` and ``Slack Token``
## Relationship between Data and Metadata
You can see metadata files in the meta directory.
A single metadata file contains rows including line location, value index and GT(GroundTruth) information about the suspect credential information for a specific repository.
Let's look at the [meta/02dfa7ec.csv](meta/02dfa7ec.csv). file as an example.
```
Id,FileID,Domain,RepoName,FilePath,LineStart,LineEnd,GroundTruth,ValueStart,ValueEnd,...
34024,61ed9af5,GitHub,02dfa7ec,data/02dfa7ec/test/61ed9af5.example,83,83,T,31,73,...
```
Convert the above line with only essential columns into a table format:
| ... | RepoName | FilePath | LineStart | LineEnd | GroundTruth | ValueStart | ValueEnd | ... | Category |
|-----|----------|-------------------------------------|-----------|---------|-------------|------------|----------|-----|-----------|
| ... | 02dfa7ec | data/02dfa7ec/test/61ed9af5.example | 83 | 83 | T | 31 | 73 | ... | Cat1:Cat2 |
This line means that the credential line exists in the 83th line of the `data/02dfa7ec/test/61ed9af5.example` file that downloaded and obfuscated output after running the `download_data.py` script.
You can find the `61ed9af5.example` file in the output generated by the download_data.py script execution by referring to the above path.
When you check the 83th line of the file, you can see that the following line exists.
```
GITHUB_ENTERPRISE_ORG_SECRET=hfbpozfhvuwgtfosmo2imqskc73w04jf3313309829
```
In here, you can see the credential based on the values of ValueStart and ValueEnd in the metadata.
```
hfbpozfhvuwgtfosmo2imqskc73w04jf3313309829
```
> That the corresponding value is an obfuscated value, not an actual credential value. For that part, please refer to the following [Obfuscation](#obfuscation) section.
With them, you can use ``review_data.py`` script to review the markup in console with colorization.
34024,61ed9af5,GitHub,02dfa7ec,data/02dfa7ec/test/61ed9af5.example,83,83,T,31,73,,,Secret:Token
83:# GITHUB_ENTERPRISE_ORG_SECRET_TOKEN=hfbpozfhvuwgtfosmo2imqskc73w04jf3313309829
## Obfuscation
If the suspicious lines are included in the dataset as it is, the credential values actually used in real may be leaked, which is not a desirable result.
To avoid such cases we proceeded:
1. Credential values obfuscation in files.
2. Directory & file name and directory hierarchy obfuscation.
### Credential values obfuscation in files
To prevent leakage of the actual credential value in the file, we can mask the line that is supposed to be credential or change it to a random string.
However, this masking and changing to a random string can make the side effects to the detection performance of several tools.
We have applied other methods to substitute the actual credential values within the limits of ensuring the detectability of these various tools.
- Replacing the real value to a example value for a case where a fixed pattern is clear (ex. AWS Access Key)
- Replacing the entire file with credential information to a example file. (ex. X.509 Key)
- Random key generation using regex pattern from the character set of real string and length.
### Directory & file name and directory hierarchy obfuscation
Even if the line suspected of having a credential in the file is obfuscated, you can easily check the original credential value and where it came from by the information of the repository (repo name, directory structure, file name).
To prevent this from happening, we obfuscated the directory structure and file names.
Files with lines suspected of being credential / detected by the tool.
It is saved in the form of `data/RepoID/(src|test)/FileID.FILE_EXTENSION`.
RepoID and FileID are not opened separately, but original repository information can be provided only for research purposes and the actual repository owner through separate contact.
For contact information, please refer to the [How to Contact](#how-to-contact) section.
## License
Each file is under the existing project's license.
It may be difficult to check the license of an existing project due to obfuscation of file and directory structure, etc., but you can check the actual license through the license corresponding to the RepoID in the license directory.
## Directory Structure
```
📦CredData
┃
┣ 📂benchmark
┃
┣ 📂data ---- To be generated by `download_data.py`
┃ ┗ 📂abcdef42
┃ ┣ 📂src
┃ ┃ ┗ 📜ad0423f5.c ---- Source File
┃ ┃
┃ ┣ 📂test
┃ ┃ ┗ 📜bebebebe.cpp ---- Source File but in the test/tests.
┃ ┃
┃ ┣ 📂other
┃ ┃ ┗ 📜c0ffee42 ---- File has no extension or Readme
┃ ┃
┃ ┗ 📜LICENSE(COPYING) ---- License File for repo A
┃
┣ 📂meta
┃ ┗ 📜abcdef42.csv
┃
┣ 📜snapshot.json ---- commit+random : URL data for repositories to be downloaded
┃
┣ 📜README.md
┃
┣ 📜download_data.py
┃
┗ 📜LICENSE
```
## Benchmark
### Main Requirements
- Python3.9 or higher
### Installation
``` bash
$ virtualenv venv
$ source venv/bin/activate
$ pip install -qr requirements.txt
```
### How to extend the dataset
1. Find an interesting repo and commit
2. add to snapshot.json the data:
``` json
"{commit_hash}{any_padding_hex_symbols_to_64}": "https://github.com/org/repo",
```
3. run download_data.py twice (first - a meta file will be created, second - all files from the commit will be downloaded)
4. run CredSweeper for the downloaded data to obtain a report (preferred with ``--ml_threshold 0`` argument)
5. run benchmark for the report with ``--fix`` option - all found values will be inserted into meta
6. review, correct markup if necessary, produce empty benchmark report for CI, commit the changes
### How to run
``` bash
usage: python -m benchmark [-h] --scanner [SCANNER]
optional arguments:
-h, --help show this help message and exit
--scanner [SCANNER] scanner name to benchmark (support: ['credsweeper', 'credential_digger',
'detect_secrets', 'gitleaks', 'shhgit', 'trufflehog3', 'wraith'])
```
Such as:
``` bash
$ python -m benchmark --scanner credsweeper
```
### Benchmark Result
A table of performance metrics for each tool tested based on CredData.
The content will be updated in detail with the release of our tool in October.
For the tools used, see the [Used Tools for Benchmarking](#used-tools-for-benchmarking) section below. (The benchmark test was conducted by combining training and test data.)
Updated: April 2022
| Name | TP | FP | TN | FN | FPR | FNR | Accuracy | Precision | Recall | F1 |
|-------------------|-------|--------|------------|-------|--------------|--------------|--------------|--------------|--------------|--------------|
| **CredSweeper** | 3,701 | 337 | 19,454,362 | 882 | 0.0000173223 | 0.1924503600 | 0.9999373564 | 0.9165428430 | 0.8075496400 | 0.8586010904 |
| credential-digger | 479 | 4,871 | 19,449,828 | 4,104 | 0.0002503765 | 0.8954833079 | 0.9995387805 | 0.0895327103 | 0.1045166921 | 0.0964461895 |
| detect-secrets | 1,748 | 10,599 | 19,444,100 | 2,835 | 0.0005448041 | 0.6185904429 | 0.9993096354 | 0.1415728517 | 0.3814095571 | 0.2064973420 |
| gitleaks | 1,120 | 1011 | 19,453,688 | 3,463 | 0.0000519669 | 0.7556185904 | 0.9997700840 | 0.5255748475 | 0.2443814096 | 0.3336312183 |
| shhgit | 330 | 306 | 19,454,393 | 4,253 | 0.0000157288 | 0.9279947633 | 0.9997657159 | 0.5188679245 | 0.0720052367 | 0.1264610079 |
| truffleHog | 42 | 126 | 19,454,573 | 4,541 | 0.0000064766 | 0.9908356971 | 0.9997601659 | 0.2500000000 | 0.0091643029 | 0.0176804883 |
| truffleHog3 | 2,507 | 14,235 | 19,440,464 | 2,076 | 0.0007316998 | 0.4529783984 | 0.9991617882 | 0.1497431609 | 0.5470216016 | 0.2351230950 |
| wraith(gitrob) | 898 | 3,099 | 19,451,600 | 3,685 | 0.0001592931 | 0.8040584770 | 0.9996513746 | 0.2246685014 | 0.1959415230 | 0.2093240093 |
- Terminology
- TP(True Positive) : Values that are Actually Positive and Predicted Positive.
- FP(False Positive): Values that are Actually Negative but Predicted Positive.
- TN(True Negative): Values that are Actually Negative and Predicted Negative.
- FN(False Negative): Values that are Actually Positive but Predicted Negative.
- FPR(False Positive Rate): False Positive / Negative, FNR and FPR should be low.
- FNR(False Negative Rate): False Negative / Positive
- Accuracy = True Positive + True Negative / Total, Accuracy, Precision and Recall should always be high.
- Precision = True Positive / Predicted Positive
- Recall = True Positive / Actually Positive
- F1 = 2 * (Precision * Recall) / (Precision + Recall)
### Used Tools for Benchmarking
Name | URL
-- | --
CredSweeper | https://github.com/Samsung/CredSweeper
credential-digger | https://github.com/SAP/credential-digger
detect-secrets | https://github.com/Yelp/detect-secrets
gitLeaks | https://github.com/zricethezav/gitleaks
shhgit | https://github.com/eth0izzle/shhgit
truffleHog | https://github.com/trufflesecurity/truffleHog
wraith(gitrob) | https://gitlab.com/gitlab-com/gl-security/security-operations/gl-redteam/gitrob
## Citation
You can use CredData on your research.
```
@misc{sr-cred21,
author = {JaeKu Yun and ShinHyung Choi and YuJeong Lee and Oleksandra Sokol and WooChul Shim and Arkadiy Melkonyan and Dmytro Kuzmenko},
title = {Project CredData: A Dataset of Credentials for Research},
howpublished ={ \url{https://github.com/Samsung/CredData}},
year = {2021}
}
```
## How to Get Involved
In addition to developing under an Open Source license, A use an Open Source Development approach, welcoming everyone to participate, contribute, and engage with each other through the project.
### Project Roles
A recognizes the following formal roles: Contributor and Maintainer. Informally, the community may organize itself and give rights and responsibilities to the necessary people to achieve its goals.
#### Contributor
A Contributor is anyone who wishes to contribute to the project, at any level. Contributors are granted the following rights, to:
- Can suggest
- Change in Ground Truth for currently added/ newly added codes
- New open repository to be included
- Report defects (bugs) and suggestions for enhancement
- Participate in the process of reviewing contributions by others
Contributors are required to:
- Must follow below rules when updating additional credential dataset
- Additional data must be individual from the original data, they must not effect(change/remove/conflict) with the original data
- Additional data must not include valid/real credential data to prevent further exposure of the credential, they must be transformed by the obfuscation rule guided in [README.md](README.md), or changed through other process which has similar obfuscation effect.
- To contribute and reflect changes, Contributors receive the approval of the maintainer.
Contributors who show dedication and skill are rewarded with additional rights and responsibilities. Their opinions weigh more when decisions are made, in a fully meritocratic fashion.
#### Maintainer
A Maintainer is a Contributor who is also responsible for knowing, directing and anticipating the needs of a given a Module. As such, Maintainers have the right to set the overall organization of the source code in the Module, and the right to participate in the decision-making. Maintainers are required to review the contributor’s requests and decide whether to accept or not the contributed data.
Name | E-Mail
-- | --
[Jaeku Yun](https://github.com/silentearth) | jk0113.yun@samsung.com
[Shinhyung Choi](https://github.com/csh519) | sh519.choi@samsung.com
[Yujeong Lee](https://github.com/yuzzyuzz) | yujeongg.lee@samsung.com
[Oleksandra Sokol](https://github.com/meanrin) | o.sokol@samsung.com
[Dmytro Kuzmenko](https://github.com/Dmitriy-NK) | d.kuzmenko@samsung.com
[Arkadiy Melkonyan](https://github.com/ARKAD97) | a.melkonyan@samsung.com
## How to Contact
Please post questions, issues, or suggestions into Issues. This is the best way to communicate with the developer.
##### True credentials by category
please, find wide info in https://raw.githubusercontent.com/Samsung/CredSweeper/main/cicd/benchmark.txt
## Data
### Selecting Target Repositories
In order to collect various cases in which credentials exist, we selected publicly accessible repositories on GitHub through the following process:
1. We wanted to collect credentials from repositories for various languages, frameworks, and topics, so we primarily collected 181 topics on GitHub.
In this process, to select widely known repositories for each topic, we limited repositories with more than a certain number of stars. 19,486 repositories were selected in this process.
2. We filtered repositories which have the license that can not be used for dataset according to the license information provided by GitHub.
In some cases, the provided license was inaccurate. So we conducted with manual review.
3. Filtering was carried out by checking whether strings related to the most common credentials such as 'password' and 'secret' among the result repositories are included and how many are included. After that, we executed several [open source credential scanning tools.](#used-tools-for-benchmarking)
4. For the results of No.3, we manually reviewed the detection results of all tools. Please check [Ground Rules for Labeling Suspected Credential Information](#ground-rules-for-labeling-suspected-credential-information) for the method used in the review.
As a result, we selected 297 repositories containing lines that we suspect had at least one credential value.
### Ground Rules for Labeling Suspected Credential Information
It is difficult to know whether a line included in the source code is a real credential.
However, based on human cognitive abilities, we can expect the possibility that the detected result contains actual credential information.
We classify the detection results to the three credential type.
- **T** (True) : It looks like a real credential value.
- **F** (False) : It looks like a false positive case, not the actual hardcoded credential value.
- **X** (Unknown/Other) : It seems that it is not a real credential but a test value, an example, or it is a placeholder.
In order to compose an accurate Ground Truth set, we proceed data review based on the following 'Ground Rules':
1. All credentials in test (example) directories should be labeled as True.
2. Credentials with obvious placeholders (`password =