goot

• goot
  • What is goot?
  • Get started
  • Use taint analysis
  • Save to neo4j
  • Use as a framework
  • Tips
  • Thanks

goot is a static analysis framework for Go. goot is easy-to-learn, easy-to-use and highly extensible, allowing you to easily develop new analyses on top of it.

Currently, goot provides the following major analysis components (and more analyses are on the way):

• Control/Data-flow analysis framework
  • Control-flow graph construction
  • Classic data-flow analyses, e.g. taint analysis
  • Your dataflow analyses

Intall goot by

go get -u github.com/cokeBeer/goot

Write code below in the project to be analysed, e.g cmd/taint/main.go

package main

import "github.com/cokeBeer/goot/pkg/example/dataflow/taint"

func main() {
	// if this file is cmd/taint/main.go
	// and you want to analyse package pkg
	// the path should be "../../pkg"
	// or "../../pkg..." for all packages under pkg
	runner := taint.NewRunner("relative/path/to/package")
	// for this project, is "github.com/cokeBeer/goot"
	runner.ModuleName = "module-name"
	runner.PassThroughDstPath = "passthrough.json"
	runner.TaintGraphDstPath = "taintgraph.json"
	runner.Run()
}

Run the code, and you will get a passthrough.json in the same directory, which contains taint passthrough information of all functions in your project
You can see key fmt.Sprintf holds a value object

{
    "fmt.Sprintf": {
        "Recv": null,
        "Results": [
            [0, 1]
        ],
        "Params": [
            [0, 1],
            [1]
        ]
    }
}

This means three things

• the first parameter's taint and the second parameter's taint are passed to the first return value,
• the first parameter receives the first parameter's taint
• the second parameter receives the second parameter's taint

Also, you will get a taintgraph.json in the same directory
You can see the json file contains taint edges from one call parameter to another call parameter

{
    "(*github.com/example/runnner.Runner).RunCmd#0#(*os/exec.Cmd).StdoutPipe#0": {
        "From": "(*github.com/example/runnner.Runner).RunCmd",
        "FromIndex": 0,
        "To": "(*os/exec.Cmd).StdoutPipe",
        "ToIndex": 0,
        "ToIsMethod": false,
        "ToIsSink": true,
        "ToIsSignature": false,
        "ToIsStatic": true
    }
}

This means there is a taint edge from position 0 of RunCmd (in this case, the parameter is the receiver runner.Runner itself ) to position 0 of StdoutPipe (in this case, the parameter is ther recevier exec.Cmd iteself, too)

To view taint edges better, you can load them to neo4j by set these parameters (for more detailed options, see options of runner)

func main() {
	runner := taint.NewRunner("../../internal...")
	runner.ModuleName = "gitlab.com/gitlab-org/gitlab-workhorse"
	// parameters about neo4j
	runner.PersistToNeo4j = true
	runner.Neo4jURI = "bolt://localhost:7687"
	runner.Neo4jUsername = "neo4j"
	runner.Neo4jPassword = "password"
	err := runner.Run()
	if err != nil {
		log.Fatal(err)
	}
}

When analysis is end, you can find nodes and taint edges in your neo4j database
For example, we run taint analysis on gitlab.com/gitlab-org/gitlab-workhorse@v13.10.0，which has a RCE vulnerability CVE-2021-22225
Using query below to find taint paths

MATCH (source:Source),(sink:Sink {name:"os/exec.CommandContext"}),p=(source)-[*7]->(sink) RETURN p

We can get a graph like this: (the red nodes are sink, the brown nodes are intra functions and the green nodes are source) Which reveals two taint paths from source to sink os/exec.CommandContext, the same as CVE-2021-22225

To use goot as a framework, first create two structs implementing pkg/toolkits/scalar.FlowAnalysis interface

// FlowAnalysis represents a flow analysis
type FlowAnalysis interface {
	GetGraph() *graph.UnitGraph
	IsForward() bool
	Computations() int
	FlowThrougth(inMap *map[any]any, unit ssa.Instruction, outMap *map[any]any)
	NewInitalFlow() *map[any]any
	EntryInitalFlow() *map[any]any
	Copy(srcMap *map[any]any, dstMap *map[any]any)
	MergeInto(Unit ssa.Instruction, inout *map[any]any, in *map[any]any)
	End(universe []*entry.Entry)
}

and pkg/golang/switcher.Switcher interface seperately

// Switcher represents a ssa instruction switcher
type Switcher interface {
	CaseAlloc(inst *ssa.Alloc)
	CasePhi(inst *ssa.Phi)
	CaseCall(inst *ssa.Call)
	CaseBinOp(inst *ssa.BinOp)
	CaseUnOp(inst *ssa.UnOp)
	...
	CaseGo(inst *ssa.Go)
	CaseDefer(inst *ssa.Defer)
	CaseSend(inst *ssa.Send)
	CaseStore(inst *ssa.Store)
	CaseMapUpdate(inst *ssa.MapUpdate)
	CaseDebugRef(inst *ssa.DebugRef)
}

Don't worry for these apis. An easy way to implement them is using compose like pkg/toolkits/scalar.BaseFlowAnalysis

// ConstantPropagationAnalysis represents a constant propagtion analysis
type ConstantPropagationAnalysis struct {
	scalar.BaseFlowAnalysis
	constantPropagationSwitcher *ConstantPropagationSwitcher
}

and pkg/golang/switcher.BaseSwitcher

// ConstantPropagationSwitcher represents a constant propagtion switcher
type ConstantPropagationSwitcher struct {
	switcher.BaseSwitcher
	constanctPropagationAnalysis *ConstantPropagationAnalysis
	inMap                        *map[any]any
	outMap                       *map[any]any
}

These can make you focus on the core methods you really need to design carefully in specific analyses
You can learn more information about how to use goot as a framework and how to run an analysis from a tiny example I prepared for you in how to use and how to run which demonstrates a constant propagation analysis

• goot's api is similar to soot, so if you wonder how goot's api work, you can learn soot first
• goot uses *map[any]any as flow and ssa.Instruction as unit, so please be careful of type assertion

• soot
• Tai-e
• tabby