Metadata-Version: 2.1
Name: evalidate
Version: 2.0.1
Summary: Validation and secure evaluation of untrusted python expressions
Home-page: http://github.com/yaroslaff/evalidate
Author: Yaroslav Polyakov
Author-email: xenon@sysattack.com
License: MIT
Description: ﻿# Evalidate
        Evalidate is simple python module for safe and very fast eval()'uating user-supplied (possible malicious) python expressions.
        
        ## Upgrade warning
        Version 2.0 is backward incompatible with older versions. safeeval and evalidate methods are removed, and EvalMode class is introduced.
        
        ## Purpose
        Originally it's developed for filtering complex data structures e.g. 
        
        Find cheap smartphones available for sale:
        ```python
        category="smartphones" and price<300 and stock>0
        ```
        
        But also, it can be used for other expressions, e.g. arithmetical, like
        ```python
        a+b-100
        ```
        
        Evalidate is fastest among all (known to me) secure eval pythong modules.
        
        ## Install
        
        ```shell
        pip3 install evalidate
        ```
            
        ## Security
        
        Built-in python features such as compile() or eval() are quite powerful to run any kind of user-supplied code, but could be insecure if used code is malicious like `os.system("rm -rf /")`. Evalidate works on whitelist principle, allowing code only if it consist only of safe operations (based on authors views about what is safe and what is not, your mileage may vary - but you can supply your list of safe operations)
        
        
        ## TL;DR. Just give me safe eval!
        ```python       
        from evalidate import Expr, EvalException
        
        src = 'a + 40 > b'
        # src = "__import__('os').system('clear')"
        
        try:
            print(Expr(src).eval({'a':10, 'b':42}))
        except EvalException as e:
            print(e)
        ```
        
        Gives output: `True`
        
        In case of dangerous code (uncomment second src line to test):
          
        output will be: `ERR: Operation type Call is not allowed`
        
        
        ## Exceptions
        Evalidate throws exceptions `CompilationException`, `ValidationException`, `ExecutionException`. All of them
        inherit from base exception class `EvalException`.
        
        ## Configure validation
        Evalidate is very flexible, depending on security model, same code can either pass validation or raise exception.
        
        EvalModel is security model class for eval - lists of allowed AST nodes, function calls, attributes and dict of imported functions. There is built-in model `base_eval_model` with basic operations allowed (which are safe from authors point of view).
        
        You can create custom empty model (and extend it later):
        ~~~python
        my_model = evalidate.EvalModel()
        ~~~
        
        (nothing is allowed by default, even `1+2` will not be considered safe)
        
        or you may start from `base_eval_mode` and extend it:
        ~~~python
        from evalidate import Expr, base_eval_model
        
        my_model = base_eval_model.clone()
        my_model.nodes.append('Mult')
        
        Expr('2*2', model=my_model).eval()
        ~~~
        
        To enable `int()` function, need to allow `'Call'` node and add this function to list of allowed function:
        
        ~~~python
        my_model.nodes.append('Call')
        my_model.allowed_functions.append('int')
        
        Expr('int(36.6)', model=my_model).eval()
        ~~~
        
        Or, to call attributes:
        ~~~python
        m = base_eval_model.clone()
        m.nodes.extend(['Call', 'Attribute'])
        m.attributes.append('startswith')
        
        src = '"abcdef".startswith("abc")'
        r = evalidate.Expr(src, model=m).eval()
        ~~~
        
        But even with this settings, exploiting it with expression like `__builtins__["eval"](1)` will fail (good!).
        
        
        ### Exporting my functions to eval code
        ~~~python
        def one():
          return 1
        
        m = base_eval_model.clone()
        m.nodes.append('Call')
        Expr('one()', model=m).eval()
        ~~~
        
        ## Improve speed by using native eval() with validated code
        Evalidate is very fast, but it's still takes CPU cycles... If you want to achieve maximal possible speed, you can use python native [eval](https://docs.python.org/3/library/functions.html#eval) with this kind of code:
        
        ~~~python
        from evalidate import Expr
        
        d = dict(a=1, b=2)
        expr = Expr('a+b')
        eval(expr.code, None, d) # <-- native python eval, will run at eval() speed
        ~~~
        
        This is as secure as expr.eval(), because `expr.code` is already validated to be secure.
        
        Difference is very little: execution of `expr.code` can throw any exception, while `expr.eval()` can throw only ExecutionException. Also, if you want to export your functions to eval, you should do this manually. 
        
        ## Limitations
        
        evalidate uses [ast.parse()](https://docs.python.org/3/library/ast.html#ast.parse) to get [AST node](https://docs.python.org/3/library/ast.html#node-classes) to validate it.
        
        >Warning
        >
        >It is possible to crash the Python interpreter with a sufficiently large/complex string due to stack depth limitations in Python’s AST compiler. 
        
        In my test, works well with 200 nested int(): `int(int(.... int(1)...))` but not with 201. Source code is 1000+ characters. But even if evalidate will get such code, it will just raise `CompilationException`.
        
        
        ### evalidate.security.test_security()
        Evalidate is very flexible and it's possible to shoot yourself in foot if you will try hard. `test_security()` checks your configuration (nodes, funcs, attrs) against given list of possible attack code or against built-in list of attacks. `test_security()` returns True if everything is OK (all attacks raised ValidationException) or False if something passed.
        
        This code will never print (I hope).
        ~~~python
        from evalidate.security import test_security
        
        test_security() or print("default rules are vulnerable!")
        ~~~
        
        But this will fail because nodes/funcs leads to successful validation for attack (suppose you do not want anyone to call `int()`)
        ~~~python
        from evalidate.security import test_security
        
        attacks = ['int(1)']
        
        test_security(attacks, addnodes=['Call'], funcs=['int'], verbose=True)
        ~~~
        
        It will print:
        ~~~
        Testing attack code:
        int(1)
        Problem! Attack passed validation without exception!
        Code:
        int(1)
        ~~~
        
        
        
        
        ## Example
        
        ### Filtering by user-supplied condition ###
        
        This is code of `examples/products.py`. Expression is validated and compiled once and executed (as byte-code, very fast) many times, so filtering is both fast and secure.
        
        
        ~~~python
        #!/usr/bin/env python3
        
        import requests
        from evalidate import Expr, ValidationException, CompilationException, ExecutionException
        import json
        import sys
        
        data = requests.get('https://dummyjson.com/products?limit=100').json()
        
        try:
            src = sys.argv[1]
        except IndexError:
            src = 'True'
        
        try:
            expr = Expr(src)
        except (ValidationException, CompilationException) as e:
            print(e)
            sys.exit(1)
        
        c=0
        for p in data['products']:
            # print(p)
            try:
                r = expr.eval(p)
                if r:
                    print(json.dumps(p, indent=2))
                    c+=1
            except ExecutionException as e:
                print("Runtime exception:", e)
        print("# {} products matches".format(c))
        ~~~
        
        ~~~shell
        # print all 100 products
        ./products.py
        
        # Only cheap products, 8 matches
        ./products.py 'price<20'
        
        # smartphones (5)
        ./products.py 'category=="smartphones"'
        
        # good smartphones
        ./products.py 'category=="smartphones" and rating>4.5'
        
        # cheap smartphones
        ./products.py 'category=="smartphones" and price<300'
        ~~~
                                               
        
        ## Similar projects and benchmark
        
        [asteval](https://newville.github.io/asteval/)
        
        While asteval can compute much more complex code (define functions, use python math libraries) it has drawbacks:
        - asteval is much slower (evalidate can be used at speed of eval() python bytecode)
        - user can provide source code which runs very long time and consumes many resources 
        
        
        [simpleeval](https://github.com/danthedeckie/simpleeval)
        Very similar project, using AST approach too and optimized to re-evaluate pre-parsed expressions. But parsed expressions are stored as more high-level [ast.Expr](https://docs.python.org/3/library/ast.html#ast.Expr) type and this approach is few times slower, while evalidate uses python native `code` type and evaluation itself goes at speed of python eval()
        
        evalidate is good to run same expression against different data.
        
        ## Benchmarking
        We use `benchmark/benchmark.py` in this repository.
        We prepare list of 1 million of products (actually, we take just 100 products sample, but repeat it 10 000 times to get 1 million), and then filter it, finding only specific products on "untrusted user-supplied expression" (`price < 20` in this case)
        
        ~~~
        Products: 1000000 items
        evalidate_raw_eval(): 0.266s
        evalidate_eval(): 0.326s
        test_simpleeval(): 1.824s
        test_asteval(): 26.106s
        ~~~
        
        As you see, evalidate is few times faster then simpleeval and both are much faster then asteval.
        
        Maybe my test is not perfectly optimized (I'm not expert with simpleeval/asteval), if you can suggest better filtering sample code (which produces faster result), I will include it. (Benchmark code must assume expression as unknown in advance and untrusted)
        
        
        ## Read about eval() risks
        
        - https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html
        - https://netsec.expert/posts/breaking-python3-eval-protections/
        - https://realpython.com/python-eval-function/
        
        Note: realpython article shows example with nice short method of validation source (using `code.co_names`), 
        but it's vulnerable, it passes "bomb" from Ned Batchelder article (bomb has empty `co_names` tuple) and crash interpreter. Evalidate can block this code and similar bombs (unless you will intentionally configure evalidate to pass specific bomb code. Yes, with evalidate it is hard to shoot yourself in the foot, but it is possible if you will try hard).
        
        ## More info
        
        Want more info? Check source code of module, it's very short and simple, easy to modify
        
        ## Contact
        
        Write me: yaroslaff at gmail.com
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Utilities
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Interpreters
Description-Content-Type: text/markdown
