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Advanced Topics

This page documents more advanced bits of Pysa.

Conditional models based on Python version​

Pysa models support if conditions but only for version comparisons for the python version used to run pysa. This allows for conditional parsing of models and allows different models to be used for different versions of python.

if sys.version == (3,9,0):
    def module.foo(): ...
else:
    def module.bar(): ...

In this example, the first model will only be parsed and honored if the python version in the system or virtual environment from which Pysa is run is equal to 3.9.0. In all other conditions, the second model will be parsed and honored.

sys.version is the only allowed left hand expression and the right hand expression has to be a tuple of integers of the form (major, minor, micro). Only the major version number is required and the other two are optional.

The comparison operators supported include == (equal to), != (not equal to), < (less than), > greater than, <= (less than or equal to), and >= (greater than or equal to).

If conditions can also be nested inside one another and follow the same behavior as python if conditions.

Obscure models​

When Pysa does not have enough information about a function or method, it will make basic assumptions about its behavior. This is referred to as an obscure model. Most notably, it assumes that the function or method propagates the taint from its arguments to its return value.

This usually happens when Pysa doesn't know about the callee of a function call:

def foo(f: Any):
    x = input()
    y = f(x) # no information about `f`, y will be considered tainted.
    eval(y)

Functions and methods defined in type stubs or in a different language (for instance, in C or C++ bindings) will also be treated as obscure models.

To prevent a function or method from being marked as obscure, one can use the @SkipObscure taint annotation in a .pysa file:

@SkipObscure
def module.foo(): ...

Parameter and return path​

When writing a model for a source, the ReturnPath annotation allows to specify which index or attribute of the returned value is tainted. For instance:

def only_attribute_foo_tainted() -> TaintSource[Test, ReturnPath[_.foo]]: ...

Similarly, the ParameterPath annotation allows to specify which index or attribute of an argument leads to a sink. For instance:

def only_arg_dot_bar_is_sink(arg: TaintSink[Test, ParameterPath[_.bar]]): ...

Access path definition​

The ParameterPath and ReturnPath annotation takes an access path as an argument. An access path starts with an underscore _ which represents the whole argument or return value (depending on the context). The underscore can be followed by attribute accesses (e.g, _.foo.bar) and index accesses (e.g, _["foo"][0]["bar"]), or a combination of both (e.g, _.foo[0]).

In addition to these, four special calls can be used: .all(), .keys(), .parameter_name() and .all_static_fields().

all()​

.all() is used to represent that any index might be tainted. This is usually when the index cannot be known statically. For instance:

def foo(i: int):
  i = random.randint(0, 100)
  return {i: source()}

This can be represented by the model:

def foo(): TaintSource[Test, ReturnPath[_.all()]]: ...

keys()​

.keys() is used to represent that any key of the dictionary might be tainted. For instance:

def foo():
  return {source(): 0}

This can be represented by the model:

def foo(): TaintSource[Test, ReturnPath[_.keys()]]: ...

all_static_fields()​

.all_static_fields() is used to mark all statically-known attributes of the given parameter or return value as a source or sink. The set of attributes is determined using the type annotation of the parameter or return value. If it is not annotated or that we could not find any attributes, the whole parameter or return value will be marked as a source or sink instead.

For instance:

class A:
  x: str
  y: str

def foo(a: A) -> B: ...

Using the following model:

def foo(a: TaintSink[Test, ParameterPath[_.all_static_fields()]]): ...

This will add a sink on a.x and a.y.

This can also be used on the return value:

def foo() -> TaintSource[Test, ReturnPath[_.all_static_fields()]]: ...

In general, we recommend to mark the whole parameter or return value as a source or sink. This feature is only useful for power users that post process the result of the analysis and extract leaf ports. It is also very computationally expensive.

parameter_name()​

.parameter_name() will be replaced by the name of the parameter that is being modelled. This can only be used for TaintInTaintOut on parameters of functions or methods. This is usually useful to model constructors of dataclass-like classes.

For instance:

class A:
  def __init__(self, x, y, z):
    # method too complicated
    pass

Using the following model query:

ModelQuery(
  name="constructors",
  find="method",
  where=[fully_qualified_name.equals("A.__init__")],
  model=[
    Parameters(TaintInTaintOut[LocalReturn, NoCollapse, ReturnPath[_.parameter_name()]]),
    Modes([SkipAnalysis])
  ]
)

This will automatically propagate taint from parameters x, y, z to self.x, self.y and self.z. Note that if the code is available and the constructor is not too complex, Pysa will do that automatically without the need for a model.

Taint In Taint Out​

ParameterPath and ReturnPath can also be used to give more information about a propagation. For instance:

def foo(arg):
  return {"a": arg["b"][42]}

This can be represented by the model:

def foo(arg: TaintInTaintOut[ParameterPath[_["b"][42]], ReturnPath[_["a"]]]): ...

Note that Pysa will automatically infer propagations if it has access to the body of the function. Writing taint-in-taint-out models should rarely be required.

When using the Updates annotation, the annotation UpdatePath is used instead of ReturnPath. For instance:

def MyClass.updates_foo(self, x: TaintInTaintOut[Updates[self], UpdatePath[_.foo]]): ...

Taint broadening​

Taint broadening is an over-approximation performed by the taint analysis for correctness or performance reasons. After applying broadening, Pysa considers that a whole object or variable is tainted when only some attributes or keys were initially tainted.

This is also called taint collapsing or tree collapsing because the taint is internally represented as a tree structure where edges are attributes or keys. Collapsing means merging the taint on all children into the root of the tree.

For instance, this happens when Pysa does not have access to the body of a function:

def obscure_function(arg): ...


def foo():
  # Only `x['a']` is tainted.
  x = {"a": source()}

  # Taint broadening happens, `y` and all its attributes are considered tainted.
  y = obscure_function(x)

  # This is considered an issue, even if only `x['a']` was initially tainted.
  sink(y['b'])

  # Also an issue, `y` is entirely tainted.
  sink(y)

Note that whenever broadening happens, Pysa will automatically add a broadening feature on the taint flow, which can help discard false positives in post processing. Fine grained features are used for each different scenario leading to broadening.

The most common causes for taint broadening are the following:

Broadening on obscure models​

Taint that flows through an obscure model - for instance, when Pysa does not have access to the body of the callee - is collapsed, since we must assume anything could get tainted, for correctness.

In this scenario, the tito-broadening and via:obscure:model features are added to the flow.

Broadening on taint-in-taint-out (TITO)​

When specifying a taint propagation (also called Taint In Taint Out or TITO) in a .pysa file, the propagation will collapse the taint by default.

For instance:

# models.pysa
def tito(arg: TaintInTaintOut): ...
def foo():
  x = {"a": source()}
  y = tito(x)
  sink(y['b']) # Considered an issue because of taint broadening.

In this scenario, the tito-broadening feature is added to the flow.

If the function is known to preserve the structure of the argument, the NoCollapse annotation can be used to disable collapsing. For instance:

def tito(arg: TaintInTaintOut[NoCollapse]): ...

This would remove the issue from the previous example.

Note that this can be used in combination with ParameterPath and ReturnPath.

Model broadening​

When the number of tainted attributes or keys hits a certain threshold, taint broadening is applied to prevent the analysis from blowing up by tracking too many values.

This is referred as Model broadening since this happens when the model (or summary) of a function is computed.

For instance, this can happen when the number of tainted key-value pairs of a dictionary hit a certain threshold. For scalability reasons, Pysa cannot track an infinite amount of indices, and thus makes the approximation that the whole object is tainted.

def foo(condition):
    d = {}
    if condition:
        d["a"] = source()
        d["b"] = source()
        # c, d, e, etc.
    else:
        d["1"] = source()
        d["2"] = source()
        # etc.
    return d # too many indexes, the whole return value is considered tainted.

In this scenario, the model-broadening feature is added to the flow.

See analysis thresholds for documentation about the different scenarios of model broadening.

Note that model broadening can be disabled for a given function or method using the @SkipModelBroadening annotation in a .pysa file:

@SkipModelBroadening
def foo(): ...

This can also be used in a ModelQuery using the Modes clause.

Note that this should be used sparingly since this can potentially lead to an increase in analysis time.

Model shaping​

When a specific attribute or key is tainted when the whole object is tainted with the same taint kind (e.g, UserControlled), taint collapsing is applied as an optimization to save analysis time. This is called model shaping and is applied right before model broadening.

For instance:

def my_sink(x):
  sink(x)
  sink(x.foo)

The sink on x.foo (represented as formal(x)[foo]) is merged into the sink on x.

Note that this is sound since attributes of a tainted object are also considered tainted. Thus if my_sink is actually called with x.foo tainted, the flow will be found as expected.

The downside is that this can lead to false positives in cases where my_sink is called with another attribute (say x.bar) tainted. We would find a flow from x.bar to x.foo in my_sink.

In this scenario, the model-shaping feature is added to the flow. The features model-source-shaping, model-sink-shaping and model-tito-shaping are also added to differentiate whether the shaping was on sources, sinks or tito.

Widen broadening​

When the number of tainted attributes or keys is potentially infinite because of a loop or recursion, taint broadening is applied to allow the termination of the analysis.

The term "widen" or "widening" refers to an operator that is applied to ensure convergence. It commonly happens within loops.

For instance:

def foo(n):
  d = {}
  for _ in range(n):
    d = {
      "a": source(),
      "b": d,
    }
  return d

Technically, d['b']...['b']['a'] (with an infinite number of access to b) could be tainted. To allow the analysis to terminate, Pysa stops at a certain depth. See analysis thresholds for documentation about the different scenarios of widen broadening.

Another example:

def foo(person):
    while person.parent is not None:
        person = person.parent
        # Infer sinks on person.name, person.parent.name, person.parent.parent.name, etc.
        sink(person.name)

In these scenarios, the widen-broadening feature is added to the flow.

Issue broadening​

When an object with a tainted attribute or key reaches a sink, Pysa considers the flow as valid even if the whole object is not tainted.

For instance:

d = {"a": source(), "b": "foo"}
sink(d) # `d` itself is not tainted, but `d["a"]` is, thus we emit an issue.

In this scenario, the issue-broadening feature is added to the issue.

Tainting Specific kwargs​

Sometimes, a function can have potential sinks mixed together with benign parameters in the keyword arguments (kwargs) that it accepts. In these cases, tainting the whole kwargs variable will result in false positives when tainted data flows into a benign kwarg. Instead, for a function like this:

def eval_and_log(**kwargs):
    eval(kwargs["eval"])
    logging.debug(kwargs["log"])

We can lie a bit in our .pysa file, and break out the dangerous argument for tainting:

def eval_and_log(*, eval: TaintSink[RemoteCodeExecution], **kwargs): ...

This allows us to catch flows only into the eval keyword argument.

Instance attributes versus class attributes​

Models can specify sources and sinks on attributes, following the type annotation syntax:

django.http.request.HttpRequest.GET: TaintSource[UserControlled]

Any access to request.GET will be tainted when request is an instance of HttpRequest or any of its children. However, note that the access to the class attribute (i.e, HttpRequest.GET) won't be considered tainted.

To specify sources and sinks on class attributes, use the __class__ prefix:

django.http.request.HttpRequest.__class__.GET: TaintSource[UserControlled]

To specify a source on both the class attribute and instance attribute, simply use both lines.

Literal String Sources And Sinks​

Some security vulnerabilities are best captured by modeling strings of a given form flowing to dangerous functions, or format strings that match a pattern getting tainted data passed in.

To mark all literal strings matching a pattern as sources, you first need to add a regular expression corresponding to the pattern to your taint.config:

{
  "sources": [
    {
      "name": "IPAddress"
    }
  ],
  "implicit_sources": {
     "literal_strings": [
       {
         "regexp": "\\d{1,3}(\\.\\d{1,3})+",
         "kind": "IPAddress",
         "description": "String that looks like an IP address."
       }
     ]
  }
}

With this regex in place, whenever Pysa sees a string such as 123.456.789.123, it will flag it as a taint source with the kind IPAddress.

def test() -> None:
    ip_address = "123.456.789.123"
    dont_pass_an_ip_address(ip_address) # Pysa will now flag this.

The converse of supporting literal strings as sinks is also supported, for data flowing into a tainted string. The syntax allows you to model data being used to format strings, like f-strings, manual string formatting, the string format() method, and printf-style string formatting with %.

Template strings and manual string formatting with more than two subexpressions are not yet supported.

To add a literal sink, first add the literal_sink to your configuration

{
  "sinks": [
    { "name": "MayBeRendered" },
    { "name": "MayBeSQL" }
  ],
  "implicit_sinks": {
     "literal_strings": [
       {
         "regexp": "^<.*>$",
         "kind": "MayBeRendered",
         "description": "Indicates a string whose contents may be rendered."
       },
       {
         "regexp": "^SELECT *.",
         "kind": "MayBeSQL",
         "description": "Indicates a string whose contents may be a SQL query."
       }

     ]
  }

Now, Pysa will treat any values flowing into a each of the following as a regular sink:

def may_render(parameter: str) -> None:
    result = f"<content={parameter}>"
    result = "<content={}>".format(parameter)
    result = "<content%s>" % (parameter,)

As well as values flowing into each of these as a regular sink:

def build_sql_query(columns: str) -> None:
    result = f"SELECT {columns} FROM users;"
    result = "SELECT {} FROM users;".format(columns)
    result = "SELECT %s FROM users" % (columns,)
    result = "SELECT " + columns + " FROM users;"

Note that string literal sinks have some limitations. For instance, they cannot catch issues if the string literal is stored in a temporary variable (e.g., x = "SELECT {}"; x.format(input)). We recommend using string combine rules instead (see below).

Combined Source Rules​

Some security vulnerabilities are better modeled as multiple sources reaching a sink. For example, leaking credentials via requests.get could be modeled as user controlled data flowing into the url parameter and credentials flowing into the params parameter. These flows can be modeled by combined source rules.

Sources for combined source rules are declared as normal in taint.config. Sinks, however, need to be unique to the combined source rule and are declared inside the rule definition. The rule itself is declared in the combined_source_rules top level entry. The rule lists all the same things as a regular rule, but also ties labels to its sources:

{
  "sources": [
    { "name": "UserControlled" },
    { "name": "Credentials" }
  ],
  "combined_source_rules": [
    {
       "name": "Credentials leaked through requests",
       "sources": { "url": "UserControlled", "creds": "Credentials" },
       "partial_sink": "UserControlledRequestWithCreds",
       "code": 1,
       "message_format": "Credentials leaked through requests",
       "main_trace_source": "url",
    }
  ]
}

Sources are declared as normal in .pysa files. Instead of specifying sinks with a TaintSink annotation, however, PartialSink annotations are used to specify where each source needs to flow for the combined source rule. These PartialSink must reference the labels that were declared in multi_sink_labels:

def requests.api.get(
  url: PartialSink[UserControlledRequestWithCreds[url]],
  params: PartialSink[UserControlledRequestWithCreds[creds]],
  **kwargs
): ...

With the above configuration, Pysa can detect cases where UserControlled flows into url and Credentials flow into params at the same time.

The optional attribute main_trace_source can be used to specify which flow should be shown as the main flow in the SAPP UI. For example, in the above rule, the flow from source UserControlled to sink UserControlledRequestWithCreds is the main flow.

The SAPP UI only shows a single flow at a time. However, an issue for a combined source rule corresponds to two flows. For example, for the above rule, an issue is filed only if there exist

  • One flow from source UserControlled to sink UserControlledRequestWithCreds, and
  • Another flow from source Credentials to sink UserControlledRequestWithCreds.

For combined source issues, Pysa will always show the main flow, and provide the secondary flow as a subtrace that can be expanded in the UI.

When attribute main_trace_source is missing, Pysa treat the sources under the first tag as the main sources.

String Combine Rules​

It is sometimes useful to detect data tainted with a source (e.g., UserControlled data) that is incorporated into a suspicious looking string (e.g., a string that looks like a SQL query). Detecting such a pattern is useful, because it allows Pysa to detect dynamic creation of SQL queries which could lead to SQL injection, even if the code under analysis is using a SQL framework that Pysa does not have models for.

To detect such flows, one can specify a variant of combined source rules, called string combine rules, to detect when the suspicious string (identified via regex match) and the other configured source both flow into string formatting call sites (such as calling str.__add__, str.__mod__, str.format or constructing f-strings).

For example, to detect flows from source UserControlled to sink StringMayBeSQL, one should specify the following contents in the taint configuration file, where UserControlled is declared as a main source and StringMayBeSQL is declared as a secondary source:

{
  "sources": [
    { "name": "UserControlled" },
    { "name": "StringMayBeSQL" }
  ],
  "implicit_sources": {
    "literal_strings": [
      {
        "regexp": "SELECT.*",
        "kind": "StringMayBeSQL",
        "comment": "matches a SQL statement"
      }
    ]
  },
  "string_combine_rules": [
    {
       "name": "User controlled data flows into potential SQL strings",
       "main_sources": "UserControlled",
       "secondary_sources": "StringMayBeSQL",
       "partial_sink": "UserControlledDataAndStringMayBeSQL",
       "code": 4324,
       "message_format": "User controlled data flows into potential SQL strings"
    }
  ]
}

As shown above, one needs to additionally specify a (unique) partial sink kind (such as UserControlledDataAndStringMayBeSQL), which will be automatically introduced onto the actual arguments at string formatting call sites.

The above rule enables catching the following flows:

def issue():
  uc = user_controlled()
  f"SELECT {uc} FROM async_query"
  "SELECT " + uc + " FROM async_query"
  "SELECT %s FROM async_query" % uc
  "SELECT {} FROM async_query".format(uc)

Note that the string combine rules are strictly more powerful than the feature of implicit literal string sinks. That is, any flow that can be detected via implicit literal string sinks can also be detected via string combine rules, but not vice versa. Hence, we recommend using the string combine rules.

Prevent Inferring Models with SkipAnalysis​

In addition to the models defined in .pysa files, Pysa will infer models for functions based what sources, sinks, etc. they call in their body. The SkipAnalysis annotation can be used to prevent Pysa from inferring models, and instead force it to use only the user defined models for determining taint flow:

@SkipAnalysis
def qualifier.dont_generate_models(argument): ...

SkipAnalysis can be applied at the class level as a shorthand to prevent pysa from infering models for all functions in a class:

class skip_analysis.SkipMe(SkipAnalysis): ...

Ignoring overrides​

When a method is called on a base class, Pysa has to assume that that call could actually invoke any subclass methods that override the base class's method. For heavily overriden methods, this can lead to both performance impacts and false positives. When running Pysa, you may see messages such as this in the output:

2020-09-02 09:25:50,677 WARNING `object.__init__` has 106 overrides, this might slow down the analysis considerably.

The above message indicates that 106 subclasses of object have overridden __init__. If Pysa sees taint flowing into object.__init__, then it will treat all 106 overrides of object.__init__ as also receiving that taint.

The @SkipOverrides decorator can be applied to deal with false positives or performance issues from having too many overrides on a given function:

@SkipOverrides
def object.__init__(self): ...

This annotation will cause Pysa not to propagate taint into to and from overridden methods on subclasses, when analyzing functions that call the overriden method on the base class.

maximum_overrides_to_analyze can be added the the options block of taint.config to limit the number of overrides that Pysa will analyze:

{
  "sources": [],
  "sinks": [],
  "features": [],
  "rules": [],
  "options": {
    "maximum_overrides_to_analyze": 60
  }
}

This option can also be provided in the command line, using --maximum-overrides-to-analyze.

This can speed up the analysis, but it will lead to false negatives, because Pysa will only propagate taint to or from 60 (in the case of the above example) overriden methods on subclasses. The remaining overriding methods will be ignored and treated as if they weren't actually overriding the base class method.

By default, Pysa skips overrides on some functions that are typically problematic. You can find the full list of default-skipped functions in stubs/taint/skipped_overrides.pysa

Force to analyze all overrides​

We also allow the use of decorator @AnalyzeAllOverrides to force analyzing all overriding methods of a given method, regardless of the configured maximum number of overrides to analyze (e.g., via command line option --maximum-overrides-to-analyze), or if there simultaneously exists an @SkipOverrides on the given method. An example is:

@AnalyzeAllOverrides
def BaseClass.method(self): ...

Decorator @AnalyzeAllOverrides is often used to reduce false negatives, by analyzing all overrides of some selected methods. This offers a more fine-grained option than tweaking --maximum-overrides-to-analyze for all methods. Adding decorator @AnalyzeAllOverrides to some selected methods is faster than using a large threshold of maximum overrides for all methods, but achieves better precision than using a small threshold.

Limit the trace length for better signal and performance​

By default, Pysa will find all flows from sources to sinks matching a rule. This can lead to very long traces which are hard to understand and tend to be false positives. This also brings down the performance a lot.

Pysa provides a --maximum-trace-length <integer> command line argument which limits the length of traces that it finds. In general, this will also make Pysa faster.

This option can also be added in the taint.config as follows:

{
  "sources": [],
  "sinks": [],
  "features": [],
  "rules": [],
  "options": {
    "maximum_trace_length": 20
  }
}

Note that this is not a silver bullet and that this might hide security vulnerabilities. Use it with caution.

Limit the trace length for a given rule​

Similarly to the option described above, one can limit the trace length for a given rule, using the filters option:

"rules": [
  {
    "name": "SQL injection.",
    "code": 1,
    "sources": [ "UserControlled" ],
    "sinks": [ "SQL" ],
    "message_format": "Data from [{$sources}] source(s) may reach [{$sinks}] sink(s)",
    "filters": {
      "maximum_source_distance": 10,
      "maximum_sink_distance": 5
    }
  }
]

This will limit the trace length from the root to the source by 10, and the trace length from the root to the sink by 5, only for that specific rule.

Note: This is meant to be used to limit the number of issues written to the database. Prefer using SAPP to filter out false positives.

Limit the tito depth for better signal and performance​

Pysa automatically infers when a function propagate the taint from one argument to its return value. This is called tito, for "Taint In Taint Out". In practice, infering it can be very expensive since the taint can go through an arbitrary number of hops (i.e, depth).

For instance:

def foo(x):
  return x
def bar(x):
  return foo(x)
def baz(x):
  return bar(x)

In this example, baz propagates the taint on its argument to the return value using 3 hops.

Pysa provides a --maximum-tito-depth <integer> command line argument which limints the depth of inferred propagations. In combination with the trace length limit, this usually makes Pysa faster.

This option can also be added in the taint.config as follows:

{
  "sources": [],
  "sinks": [],
  "features": [],
  "rules": [],
  "options": {
    "maximum_tito_depth": 20
  }
}

Decorators​

By default, Pysa does not generally understand decorators, and will treat a call to a decorated function as an obscure call.

This will usually lead to false negatives if the decorated functions has sources or sinks. For instance:

def identity(f: Callable[[str], None]) -> Callable[[str], None]:
  return f

@identity
def decorated_sink(x: str) -> None:
  sink(x)

decorated_sink(source()) # False negative, issue will NOT be found!

This also leads to false negatives if the decorator adds a flow to a sink. For instance:

def with_sink(f: Callable[[str], None]) -> Callable[[str], None]:
  def inner(x: str) -> None:
    sink(x)
    f(x)

  return inner

@with_sink
def foo(x: str) -> None:
  print(x)

foo(source()) # False negative, issue will NOT be found!

Since the call to a decorated function is treated as an obscure call, it will conservatively propagate taint through decorated function:

def identity(f: Callable[[str], str]) -> Callable[[str], str]:
  return f

@identity
def decorated(x: str) -> str:
  # Whatever happens here will not be considered at the call site.
  return 'hello %s' % x

sink(decorated(source())) # Issue is properly found.

Pysa provides a few ways to deal with these limitations.

Ignoring decorators​

Pysa can entirely ignore a decorator, as if it was not present in the source code. This can be done safely when the decorator does not change the signature of the decorated function (i.e, it does not add or remove parameters).

To ignore a decorator, use the @IgnoreDecorator annotation in a .pysa file:

@IgnoreDecorator
def module.decorator(): ...

Inlining decorators​

Pysa can try to inline decorators into decorated functions before analyzing them. This can be enabled with the --inline-decorators flag.

Inlining will take the code of the decorator and copy it within the decorated function. For instance:

def my_decorator(f: Callable[[int], int]) -> Callable[[int], int]:
  def inner(x: int) -> int:
    before(x)
    result = f(x)
    after(x)
    return result

  return inner

@my_decorator
def decorated(x: int) -> int:
  return x + 1

Will be inlined as:

def decorated(x: int) -> int:
  before(x)
  result = x + 1
  after(x)
  return result

Prevent Inlining Decorators with SkipDecoratorWhenInlining​

Decorator inlining comes at the cost of increasing the analysis time and also increasing the lengths of traces. If you would like to prevent certain decorators from being inlined, you can mark them in your .pysa file using @SkipDecoratorWhenInlining:

# foo.pysa
@SkipDecoratorWhenInlining
def foo.decorator_to_be_skipped(f): ...
# foo.py
@decorator_to_be_skipped
def bar(x: int) -> None:
  pass

This will prevent the decorator from being inlined when analyzing bar. Note that we use @SkipDecoratorWhenInlining on the decorator that is to be skipped, not the function on which the decorator is applied.

Unfortunately, this will lead back to false negatives as described earlier.

For instance:

@decorator_to_be_skipped
def bar(x: int) -> None:
  sink(x)

bar(source()) # False negative, issue will NOT be found!

Single trace sanitizers with @SanitizeSingleTrace​

Sanitizers, as described in the Overview, are applied in both the forward (i.e source) trace and backward (i.e sink) trace.

For instance, with the given .pysa file:

@Sanitize(TaintInTaintOut[TaintSink[RemoteCodeExecution]])
def shlex.quote(x): ...

And the following Python code:

import subprocess
from shlex import quote

def quoted_input():
  x = input() # source 'UserControlled'
  y = quote(x)
  return y

def echo(argument):
  subprocess.run(f'/bin/echo {argument}', shell=True) # sink 'RemoteCodeExecution'

def issue():
  x = quoted_input() # source trace: input -> quoted_input -> issue
  echo(x) # sink trace: issue -> echo -> subprocess.run

Pysa will NOT find an issue here, as expected. This is because during the propagation of the 'UserControlled' source in the forward trace, pysa remembers that it was sanitized for the sink 'RemoteCodeExecution'.

However, Pysa provides a simpler version of sanitizers, which only sanitizes in the forward trace or the backward trace:

@SanitizeSingleTrace(TaintSource)
def f(): ...

@SanitizeSingleTrace(TaintSource[UserControlled])
def g(): ...

@SanitizeSingleTrace(TaintSink)
def h(): ...

@SanitizeSingleTrace(TaintSink[RemoteCodeExecution])
def i(): ...

These sanitizers are a lot cheaper and could save analysis time. However, these might introduce false positives, so we recommend to use the default sanitizers.

Filtering the call graph with @Entrypoint​

By default, Pysa will analyze the entire call graph of your program. This can lead to longer analysis times for larger programs, especially when you'd only like to perform analysis on specific parts of the program. This decorator will mark a specified function and the functions it calls as the only functions to be analyzed.

Note: the flag --limit-entrypoints must be passed to pyre analyze for call graph filtering to occur, even if the @Entrypoint decorator is present. This allows for call graph filtering to be easily enabled or disabled without editing your .pysa files.

If you have the following Python file:

class MyClass:
  def class_entrypoint():
    taint_sink(taint_source())

def my_bad_func_1():
  taint_sink(taint_source())

def my_bad_func_2():
  taint_sink(taint_source())

def func_entrypoint():
  my_bad_func_1()

def main():
  func_entrypoint()
  my_bad_func_2()
  MyClass().class_entrypoint()

main()

And the following .pysa file:

@Entrypoint
def my_file.MyClass.class_entrypoint(): ...

@Entrypoint
def func_entrypoint(): ...

Then issues will be found for taint in calls to class_entrypoint and my_bad_func_1, but not my_bad_func_2, since it isn't called by a function marked by an @Entrypoint.

Taint In Taint Out Transforms​

Taint in taint out transforms can be used to capture more precise flows.

As an example:

def read_file(path):
  with open(path, "r") as f:
    content = f.read()
  return content

Without taint in taint transforms we can write a rule that captures a UserControlled path is read. Such a rule can be made much higher signal if we can detect that content is also ReturnedToUser. We can use taint in taint out transforms to stitch the two flows together. We mark read with a taint in taint out transform FileRead, and the rule becomes UserControlled -> FileRead -> ReturnedToUser.

To contrast with feature annotations, there are two differences:

  • The filtering is done during analysis itself, and limits the issues generated (as opposed to a post-processing step by the user)
  • Taint in taint out transforms can be used to reason about the order of events

Syntax​

In taint.config, one can specify transforms to define new transforms. Each transform is defined by following fields:

  • name: name of the transform, this is used when defining rules, as well as writing models
  • comment: description of the transform
{
  ...
  "transforms": [
    {
      "name": "MyTransform",
      "comment": "This is my transform"
    },
    ...
  ],
  ...
}

Then, one may use these transforms in rules as follows:

 {
  ...
  "rules": [
    {
      "name": ...,
      "code": ...,
      "sources": ["SourceA"],
      "transforms": ["MyTransform1", "MyTransform2"],
      "sinks": ["SinkB"],
      "message_format": "[{$sources}] transformed by [${transforms}] may reach [${sinks}]"
    },
    ...
  ],
  ...
}

Intuitively, one can think of the rule above as SourceA -> MyTransform1 -> MyTransform2 -> SinkB. The order is important.

Finally, in .pysa model files a taint transform can be specified using a TaintInTaintOut[Transform[...]] annotation, where the parameter is the name of the transform.

def my_function(arg: TaintInTaintOut[Transform[MyTransform]]): ...

Semantics​

  y = my_function(x)

If x has source taint SourceA, the taint of y is MyTransform:SourceA. This will correspond to matching SourceA -> MyTransform in a rule. Likewise, if y has sink taint SinkB, then the taint of x is MyTransorm:SinkB. This will correspond to matching MyTransform -> SinkB in a rule.

Note that a transform modifies the taint itself. Hence, if a flow passes through a transform, it will no longer match rules which do not contain the transform.

RuleX: SourceA -> SinkB
RuleY: SourceA -> MyTransform -> SinkB
Flow1: SourceA -> SinkB
Flow2: SourceA -> MyTransform -> SinkB

Flow1 matches RuleX but not RuleY. Flow2 matches RuleY but not RuleX.

Consider the scenario where we have an additional rule:

RuleZ: SourceC -> SinkD

If transform MyTransform is applied to taint SourceC, there is no possible rule it can possibly match. As an optimization, we check for this continuously in our analysis and filter out eagerly.

Also note that the existing TaintInTaintOut annotation semantics of TITO being assumed (instead of inferred) on the argument are unchanged.

Analysis thresholds​

Pysa provides many options to fine tune the taint analysis. The following options can be provided either via the command line or in the taint.config file, under the options section.

For instance:

{
  "sources": [],
  "sinks": [],
  "features": [],
  "rules": [],
  "options": {
    "maximum_model_source_tree_width": 10,
    "maximum_model_sink_tree_width": 10,
    "maximum_model_tito_tree_width": 10
  }
}

When not provided, these are set to the following defaults:

maximum_model_source_tree_width = 25;
maximum_model_sink_tree_width = 25;
maximum_model_tito_tree_width = 5;
maximum_tree_depth_after_widening = 4;
maximum_return_access_path_width = 10;
maximum_return_access_path_depth_after_widening = 4;
maximum_tito_collapse_depth = 4;
maximum_tito_positions = 50;

Maximum model source tree width​

  • Command line option: --maximum-model-source-tree-width
  • taint.config option: maximum_model_source_tree_width

See taint broadening and model broadening.

This limits the width of the source tree in the model for a callable, i.e the number of output paths in the return value.

For instance:

def foo():
  return {"a": source(), "b": source(), "c": source()}

The source tree for foo has a width of 3. Above the provided threshold, pysa will collapse the taint and consider the whole dictionary tainted. When that happens, the breadcrumbs model-broadening and model-source-broadening will be added to the flow.

Maximum model sink tree width​

  • Command line option: --maximum-model-sink-tree-width
  • taint.config option: maximum_model_sink_tree_width

See taint broadening and model broadening.

This limits the width of the sink tree in the model for a callable, i.e the number of input paths leading to a sink for a given parameter.

For instance:

def foo(arg):
  sink(arg[1])
  sink(arg[2])
  sink(arg[3])

The sink tree for foo and parameter arg has a width of 3. Above the provided threshold, pysa will collapse the taint and consider that the whole argument leads to a sink. When that happens, the breadcrumbs model-broadening and model-sink-broadening will be added to the flow.

Maximum model tito tree width​

  • Command line option: --maximum-model-tito-tree-width
  • taint.config option: maximum_model_tito_tree_width

See taint broadening and model broadening.

This limits the width of the taint-in-taint-out tree in the model for a callable, i.e the number of input paths propagated to the return value, for a given parameter.

For instance:

def foo(arg):
  return '%s:%s:%s' % (arg.a, arg.b, arg.c)

The taint-in-taint-out tree for foo and parameter arg has a width of 3. Above the provided threshold, pysa will collapse the taint and consider that the taint on the whole argument is propagated to the return value. When that happens, the breadcrumbs model-broadening and model-tito-broadening will be added to the flow.

Maximum tree depth after widening​

  • Command line option: --maximum-tree-depth-after-widening
  • taint.config option: maximum_tree_depth_after_widening

See taint broadening and widen broadening.

This limits the depth of the source, sink and tito trees within loops, i.e the length of source, sink and tito paths for each variables.

For instance:

def foo():
  variable = MyClass()
  for x in generate():
    variable.a.b.c = source()
  return result

The source tree for variable has a depth of 3 (i.e, a -> b -> c). Within a loop, pysa limits the depth to the provided threshold. For instance, if that threshold is 1, we would consider that variable.a is entirely tainted. When that happens, the breadcrumb widen-broadening will be added to the flow.

Maximum return access path width​

  • Command line option: --maximum-return-access-path-width
  • taint.config option: maximum_return_access_path_width

See taint broadening and model broadening.

This limits the width of the return access path tree in the model for a callable, i.e the number of output paths propagated to the return value, for a given parameter.

For instance:

def foo(arg):
  return {'a': arg, 'b': arg, 'c': arg}

The return access path tree for foo and parameter arg has a width of 3. Above the provided threshold, pysa will collapse the taint and consider that the whole return value is tainted whenever arg is tainted. When that happens, the breadcrumbs model-broadening and model-tito-broadering will be added to the flow.

Maximum return access path depth after widening​

  • Command line option: --maximum-return-access-path-depth-after-widening
  • taint.config option: maximum_return_access_path_depth_after_widening

See taint broadening and widen broadening.

This limits the depth of the return access path tree within loops, i.e the length of output paths propagated to the return value, for a given parameter.

For instance:

def foo(arg):
  result = MyClass()
  for x in generate():
    result.a.b.c = arg
  return result

The return access path tree for foo and parameter arg has a depth of 3 (i.e, a -> b -> c). Within a loop, pysa limits the depth to the provided threshold. For instance, if that threshold is 2, we would cut the output path to just a.b. When that happens, the breadcrumb model-broadening and model-tito-broadening will be added to the flow.

Maximum tito collapse depth​

  • Command line option: --maximum-tito-collapse-depth
  • taint.config option: maximum_tito_collapse_depth

This limits the depth of the taint tree after applying taint-in-taint-out, i.e the length of paths for taint propagated from a parameter to the return value.

For instance:

def identity(arg): return arg

def foo():
  input = {'a': {'b': {'c': source()}}}
  output = identity(input)

The taint tree for input has a depth of 3 (i.e, a -> b -> c). When the taint is propagated to the return value of identity, we limit the resulting taint tree to the given depth. For instance, if that threshold is 1, we would consider that output['a'] is tainted.

This is also applied for sinks in the backward analysis:

def foo(arg):
  output = identity(arg)
  sink(output['a']['b']['c'])

With a threshold of 1, we would consider that output['a'] leads to a sink.

Maximum tito positions​

  • Command line option: --maximum-tito-positions
  • taint.config option: maximum_tito_positions

This limits the number of positions to keep track of when propagating taint.

When taint is propagated through a function and returned (i.e, taint-in-taint-out), pysa will keep track of the position of the argument, and display it in the trace.

For instance:

def foo():
  x = source()
  y = tito(x)
           ^
  z = {"a": y}
            ^
  sink(z)

In this example, we have 2 tito positions. Above the provided threshold, pysa simply discards all positions. Note that the taint is still propagated.