Automated Signature and Policy Generation

Douglas S. Reeves

MURI Annual MeetingOctober 29, 2013

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Past Work: NSDMiner

•Automated discovery of network service dependencies, based on passive observation of network traffic

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Recent Work: MetaSymploit• Goals for malware analysis

• Faster signature generation (less time from release of exploit to availability of signature)

• High quality signatures + efficient pattern matching

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• All-in-one framework with built-in components provide rich attack-generation capabilities

• Written in scripting languages (Ruby, Python, PHP…)

• Development / deployment of attacks + variants + combinations much faster and easier than development of patches

Script-Based Attack “Factories”

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Ex: Metasploit

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Script Example1. Probe Target

Port scanning, Fingerprinting, etc.

2. Compose Attack PayloadIncludes shellcode, junk, target-specific vul bytes, etc.

3. Send PayloadTrigger vulnerability

4. Post ExploitWait for shellcode to be executed, backdoor channel created, etc.

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• First system for attack script analysis– Automatic IDS signature generation from source code

• Features– Based on symbolic execution– Only a few minutes to analyze new attack scripts and

generate signatures: “day-one defenses”

MetaSymploit

MetaSymploitAttack Scripts

IDS Signatures

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Script-based Attack Framework & Scripting Language Interpreter

Symbolic API Extension

Behavior & Constraint Logging

Output API Hooking

Constant Pattern Extracting

Pattern Refining & Consolidating

Pattern Context Deriving

Symbolic Execution Layer (SymExeLayer)

Attack Script

Attack Payloads Behavioral API Calls & Attack Constraints

Signature Generation (SigGen)

Extracted Patterns Pattern Context

IDS Signatures

Symbolically executedArchitecture

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Symbolic Execution Layer• “Symbolize” APIs related to environment and

dynamic content• Record behavioral APIs and attack branching

conditions• Hook output API to capture the entire attack payload

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Script ExampleSymbolic APIs:probe_ver()shellcode()rand_alpha()

Behavior & Constraint Logging:probe_ver()sym_ver == 5shellcode() & get_target_ret()

Hook output API:sock.put(payload)

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• Extract signature patterns for specific attack payload (e.g., constant bytes, length, offset)

• Refine patterns to filter out benign/trivial patterns, avoid duplicates

• Derive semantic context of patterns by analyzing behaviors and constraints

Signature Generation Layer

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alert tcp any any -> any 617 (

msg:“script: type77.rb (Win), target_version: 5, behavior: probe_version, stack_overflow, JMP to Shellcode with vulnerable_ret_addr";

content:"|e9 38 6c fb ff ff eb f9 ad 32 aa 71|";

pcre:"/[.]{1167}\xe9\x38\x6c\xfb\xff\xff\xeb\xf9\xad\x32\xaa\x71[a-zA-Z]{2917}/";

classtype:shellcode-detect; sid:5000656; )

Example of SignatureLine 23: payload => [ <sym_shellcode, len=sym_integer>,<sym_rand_alpha, len=(1167-sym_integer)>,<"\xe9\x38\x6c\xfb\xff\xff\xeb\xf9\xad\x32\xaa\x71", 12>,<sym_rand_alpha, 2917> ]

red is symbolic value, green is concrete value

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• We developed a lightweight symbolic execution engine for Ruby– No modification to Ruby interpreter required

• Integrated MetaSymploit into Metasploit Console as a simple command

• Output is Snort rules (signatures)• Gecode/R & HAMPI used as constraint solvers• Currently applied to 10 popular built-in components in

Metasploit: Tcp, Udp, Ftp, Http, Imap, Exe, Seh, Omelet, Egghunter, Brute

Implementation

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• Tested 548 attack scripts

• Average symbolic execution time:Less than 1 minute

Evaluation: Speed and Completion

Category Num Percentage Manual ModifyAutomatically Executed 509 92.88% NoSymbolic Loop 9 1.64% Avg 10 LOC/per scriptNon-Symbolic-Extended API Call 12 2.19% Avg 3 LOC/per scriptObfuscation & Encryption 13 2.37% Not SupportedMulti-threading 3 0.55% Not Supported

Bug in Scripts 2 0.37% Change 2 LOC

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Evaluation: Detection Rate

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Evaluation: Detection Rate

• Tested signatures on 45 Metasploit attack scripts targeting 45 vulnerable applications from exploit-db.com

• Results– 100% detection rate with generated signatures

– 0% false positive rate on “normal” network traffic (collected in our department)

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Evaluation: Comparison with Public Ruleset

• From 11/2012 Snort ruleset, only 22 out of 45 scripts had corresponding official Snort rules (based on CVE analysis)

Pattern comparison between 53 MetaSymploit generatedrules and 50 official Snort rules for 22 Metasploit attack scripts

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• Snort ruleset 07/2013 has more rules to cover Metasploit-generated exploits– Including Meterpreter shellcode– Example specific rules: exploit-kit.rulesmalware-tools.rules

• Good news?

Evaluation: Comparison (cont’d)

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Discussion• Fast, successful, accurate automated signature

generation for scripting-based exploits• Limitations

• Requires source code

• Standard limitations of symbolic execution: loops, path explosion, constraint solvers

• Cannot handle multi-threaded attacks

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Future Work: Test-Driven Security Policy Generation

• SEAndroid is currently being merged into AOSP–goal is to reduce the attack surface using least-privilege policy

• Challenge: (human) effort required to write suitable MAC policies for a particular platform and applications

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Current status of SEAndroid Policies• Current policy ruleset is manually written by NSA SEAndroid team

–793 allow rules

• Categorizes apps in a very coarse-grained way for simplicity

• Difficult to adapt rules for new platforms (ex.: The current ruleset breaks “Enforcing” mode for Nexus 7)

• The community often argues whether a new rule is correctly written

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Proposed Approach Automatically generate MAC policy from functional

tests provided by the developers Not intended to be comprehensive ruleset; instead, a major

head start on creating rules Writing test cases is already an essential step in app

deployment; policy generation is “free” Test cases exercise expected use and correct behavior of an

app System apps and middleware framework are already

equipped with rich tests in AOSP

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Proposed WorkFlow

JUnit Test Suites

Static Parser SEAndroid Test Runner

JUnit Test Suites Android Middleware

The tested app

Linux Kernel SEAndroid (Audit Mode)

SEAndroid Policy Rule Generator

Semantics of Test Cases

Runtime Behaviors of Middleware/Kernel APIs

Auto-Generated SEAndroid Policy Rules for this App

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Assumptions• Developers are benign, and conscientiously provide

test cases with high coverage– Should be true for system and platform developers, not

necessarily true for 3rd party application developers

• Generated policies should be sound, won’t be complete, but…– Too many policy rules?

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Example We processed the test suite of the Gallery app that

invokes Camera functionality to take and store photos The test suite CameraTestRunner contains 3 test

classes (13 test methods) These tests cover all camera activities, including

image storage

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Example (cont'd) The test code and audit trace logs were analyzed to

generate SEAndroid policy (only partially automated):

–allow gallery3d_app mediaserver:binder call;

–allow gallery3d_app servicemanager:binder call;

–allow gallery3d_app system_server:binder { transfer call };

–allow gallery3d_app media_app:binder { transfer call };

–allow gallery3d_app media_app:fd use;

– …(29 rules generated)

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Challenges How to distinguish runtime contexts between the

execution of test code and target app How to handle mock / fake / isolated

Content / ContentProvider used in test cases How to aggregate / generalize policy rules derived

from test cases (reduce ruleset size)