Automated Signature and Policy Generation Douglas S. Reeves

MURI Annual Meeting October 29, 2013

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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Script-Based Attack “Factories”

• 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

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Ex: Metasploit

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Script Example

1. Probe Target Port scanning, Fingerprinting, etc.

2. Compose Attack Payload Includes shellcode, junk, target-specific vul bytes, etc. 3. Send Payload Trigger vulnerability 4. Post Exploit Wait for shellcode to be executed, backdoor channel created, etc.

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MetaSymploit

• 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” Attack Scripts

MetaSymploit

IDS Signatures

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Attack Script

Architecture

Symbolically executed Symbolic API Extension Behavior & Constraint Logging Output API Hooking Symbolic Execution Layer (SymExeLayer) Script-based Attack Framework & Scripting Language Interpreter Attack Payloads Behavioral API Calls & Attack Constraints Constant Pattern Extracting Pattern Refining & Consolidating Pattern Context Signature Generation (SigGen) Deriving Extracted Patterns Pattern Context IDS Signatures

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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 Example

Symbolic APIs: probe_ver() shellcode() rand_alpha() Behavior & Constraint Logging: probe_ver() sym_ver == 5 shellcode() & get_target_ret() Hook output API: sock.put(payload) 10

Signature Generation Layer

• 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

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Example of Signature

Line 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

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\xa d\x32\xaa\x71[a-zA-Z]{2917}/

";

classtype

:shellcode-detect;

sid

:5000656; )

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Implementation

• 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

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Evaluation: Speed and Completion

• Tested 548 attack scripts • Average symbolic execution time: Less than 1 minute

Category

Automatically Executed Symbolic Loop Non-Symbolic-Extended API Call Obfuscation & Encryption Multi-threading

Num

509

Percentage

92.88% 9 12 13 3 2.37% 0.55%

Manual Modify

No 1.64% Avg 10 LOC/per script 2.19% Avg 3 LOC/per script Not Supported 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 generated rules and 50 official Snort rules for 22 Metasploit attack scripts

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Evaluation: Comparison (cont’d)

• Snort ruleset 07/2013 has more rules to cover Metasploit-generated exploits – Including

Meterpreter

shellcode – Example specific rules:

exploit-kit.rules

malware-tools.rules

• Good news?

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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 22

Proposed WorkFlow

JUnit Test Suites Static Parser JUnit Test Suites The tested app Android Middleware Linux Kernel SEAndroid Test Runner SEAndroid (Audit Mode) Semantics of Test Cases Runtime Behaviors of Middleware/Kernel APIs SEAndroid Policy Rule Generator Auto-Generated SEAndroid Policy Rules for this App 23

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 25

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) 27