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GPThreats: Fully-automated AI-generated malware and its security risks

Marcus Botacin, profile picture
Marcus Botacin

The document discusses the emergence of fully-automated AI-generated malware and its associated security risks, focusing on techniques leveraging GPT technology for malicious purposes. It outlines different types of attacks, including the use of Windows API and the development of a 'malicious copilot' for automated evasive strategies. The document emphasizes the challenges and considerations for both attackers and defenders when dealing with these advancements in malware creation.

Science
Related topics:
Large Language Model
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GPThreats Fully-automated AI-generated malware and its security risks
Introduction The first attack A newer attack Moving Forward Conclusion Whoami Education Assistant Professor @ TAMU (Since 2022) CS PhD @ UFPR, Brazil (2021) CSE/ECE BSc. + CS MSC @ UNICAMP, Brazil (2015, 2017) Research Malware at high-level: ML-based detectors. Malware at mid-level: Sandboxes and tracers. Malware at low-level: HW-based detectors. Current Project NSF SaTC: Hardware Performance Counters as the next-gen AVs. GPThreats: Fully-automated AI-generated malware and its security risks 2 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 3 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 4 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence GPT-3: Threats Figure: Source: https://research.nccgroup.com/2021/12/31/on-the-malicious-use- of-large-language-models-like-gpt-3/ GPThreats: Fully-automated AI-generated malware and its security risks 5 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Is it a real threat? GPThreats: Fully-automated AI-generated malware and its security risks 6 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence GPT-3: Threats Figure: Source: https://research.checkpoint.com/2023/o pwnai-cybercriminals-starting-to-use-chatgpt/ GPThreats: Fully-automated AI-generated malware and its security risks 7 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence How would attackers use LLMs? GPThreats: Fully-automated AI-generated malware and its security risks 8 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Exploit Kits GPThreats: Fully-automated AI-generated malware and its security risks 9 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 10 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware ChatGPT: Prompt Protection GPThreats: Fully-automated AI-generated malware and its security risks 11 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware GPT-3: Playground Figure: Source: https://platform.openai.com/playground GPThreats: Fully-automated AI-generated malware and its security risks 12 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware GPT-3: API Figure: Source: https://github.com/openai/openai-python GPThreats: Fully-automated AI-generated malware and its security risks 13 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Playground: Textual Issues GPThreats: Fully-automated AI-generated malware and its security risks 14 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Playground: Coding issues GPThreats: Fully-automated AI-generated malware and its security risks 15 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 16 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Supported Functions libeay32.dll mtxoci.dll nddeapi.dll shfolder.dll glu32.dll pstorec.dll borlndmm.dll hid.dll libcurl.dll ddraw.dll authz.dll imm32.dll libxml2.dll duilib.dll libusb-1.0.dll ws2_32.dll gdiplus.dll wtsapi32.dll pdh.dll opengl32.dll winhttp.dll mpr.dll activeds.dll vdmdbg.dll dnsapi.dll esent.dll icmp.dll mapi32.dll msvcrt.dll kernel32.dll msacm32.dll version.dll user32.dll rpcrt4.dll winsta.dll advapi32.dll setupapi.dll avifil32.dll cryptui.dll dbghelp.dll uxtheme.dll gdi32.dll wininet.dll winmm.dll iphlpapi.dll shell32.dll samlib.dll crypt32.dll ntdll.dll psapi.dll winscard.dll fltlib.dll credui.dll wsock32.dll winspool.drv netapi32.dll comctl32.dll rasapi32.dll oleaut32.dll jli.dll wintrust.dll shlwapi.dll userenv.dll ole32.dll usp10.dll util.dll comdlg32.dll dllg2.dll msvbvm60.dll oleacc.dll ntoskrnl.exe mobsync.dll imagehlp.dll nvcuda.dll secur32.dll mprapi.dll wbemcomn.dll cmutil.dll msvcr120.dll Libraries 0 10 20 30 40 50 60 70 80 90 100 Supported Functions (%) Library Support Measurement Figure: Supported functions vs. libraries. Some libraries present more functions supported by GPT-3 than others. GPThreats: Fully-automated AI-generated malware and its security risks 17 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Function Support vs. Popularity 0 10 20 30 40 50 60 70 80 90 100 Sample Frequency (%) Supported Not Supported Rarely-Used Frequentely-Used Figure: Function support vs. prevalence. There is a reasonable number of GPT-3-supported frequently used functions. GPThreats: Fully-automated AI-generated malware and its security risks 18 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 19 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Building Blocks Table: Supported Functions and Malicious Behaviors. Id Functions (tuple) Subsystem Malicious Use Behavior Name Behavior Class API LoCs 1 OpenFile FileSystem Load payload from file Payload Execution 2 12 ReadFile Loading CloseFile 2 IsDebuggerPresent Utils Check if not running Debugger Targeting 1 5 AdjustTokenPrivileges Security in an analysis environment Identification SetWindowsHookEx Data Acquisition before being malicious 3 OpenFile FileSystem Delete a referenced file Remove File Evidence 1 5 DeleteFile Removal CreateFile 4 DeleteFile FileSystem Remove own binary Delete Itself Evidence 2 10 GetFileSize FileSystem Removal GetModuleName Process 5 RegSetValueKeyExA Registry Set its own path AutoRun Persistence 4 28 GetModuleFilePath Process in the AutoRun entry RegOpenKeyA Registry GPThreats: Fully-automated AI-generated malware and its security risks 20 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Building Blocks Table: Supported Functions and Malicious Behaviors. Id Functions (tuple) Subsystem Malicious Use Behavior Name Behavior Class API LoCs 6 CryptBinarytoStringA Utils Decode payload Base64 Obfuscation 4 12 URLDownloadToFile Network retrieved from the Internet WriteFile FileSystem saving to a file 7 VirtualAlloc Memory Write a payload DLL Injection Injection 12 37 WriteProcessMemory Memory in another process CreateRemoteThread Process memory space 8 VirtualProtect Memory Set page permission Memory Run Arbitrary 2 6 CreateMutex Synchronization to run a payload Execution CloseFile FileSystem directly from memory 9 N/A N/A encode a string using XOR String XORing Obfuscation 0 10 10 N/A N/A Check CPU model via CPUID CPUID check Targeting 2 9 GPThreats: Fully-automated AI-generated malware and its security risks 21 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Is creating building blocks straightforward? The Challenges GPThreats: Fully-automated AI-generated malware and its security risks 22 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Instructing Building Blocks Creation Table: Model Commands. Commands given to the model to avoid frequent model biases. Command Goal Put in a function Avoid coding in the main Code for Windows Avoid coding for Linux Function in C Avoid producing javascript Use the Windows API Avoid using C++ internals Use the prototype f() Facilitate Integration GPThreats: Fully-automated AI-generated malware and its security risks 23 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Instructing Building Blocks Creation Table: Systematic Errors. Undesired constructions that can be easily fixed by the attackers. Error Fix ASCII vs. UNICODE Replace A and W Missing headers Add fixed set of headers C vs. C++ print using cout vs printf Missing definitions Pre-defined definitions Explicit casts Disable Warnings Excessive prints Statement removal GPThreats: Fully-automated AI-generated malware and its security risks 24 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Building Blocks Examples 1 // Code in C 2 // Code for Windows 3 // Use the Windows API 4 // Write a function 5 // Function prototype is: BOOL CPU_Identification (); 6 // Identifies if running under an Intel CPU or not Code 1: CPU Identification Prompt. GPThreats: Fully-automated AI-generated malware and its security risks 25 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 BOOL CPU_Identification (){ 2 int CPUInfo [4]; 3 __cpuid(CPUInfo , 0); 4 if (! strcmp (( char *)&CPUInfo [1], "uneG")) 5 return 1; Code 2: CPU identification via CPUID. 1 BOOL CPU_Identification () { 2 if ( IsProcessorFeaturePresent ( PF_XMMI64_INSTRUCTIONS_AVAILABLE )) 3 return TRUE; Code 3: CPU identification via processor feature. GPThreats: Fully-automated AI-generated malware and its security risks 26 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 // Code in C 2 // Code for Windows 3 // Use the Windows API 4 // Write a function 5 // Function prototype is: BOOL Debugger_Identification (); 6 // Identifies if running under a debugger or not Code 4: Debugger Identification Prompt. GPThreats: Fully-automated AI-generated malware and its security risks 27 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 bool Debugger_Identification () { 2 PEB peb; 3 __asm { mov eax , fs: [0x30]; 4 mov peb , eax; } 5 return (peb.BeingDebugged == 1) ? true : false; Code 5: Debugger detection in 32-bit systems. 1 bool Debugger_Identification () { 2 PEB peb; 3 __asm { mov rax , gs: [0x60]; 4 mov peb , rax; } 5 return (peb.BeingDebugged == 1) ? true : false; Code 6: Debugger detection in 64-bit systems. GPThreats: Fully-automated AI-generated malware and its security risks 28 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Samples Creation & Functionality Testing Table: Building Block Generation. Compilation and Sandboxing success rates, first occurence of a functional code, and code generation time. Behavior Compilable Functional First Time (s) String XORing 88% 70% 4 2,49 Debugger Identification 84% 10% 2 2,63 Remove File 95% 90% 2 2,17 Payload Loading 91% 40% 2 3,21 CPUID check 83% 30% 2 3,45 Delete Itself 94% 40% 3 2,36 Memory Run 60% 20% 2 2,11 AutoRun 99% 20% 5 2,41 Base64 60% 10% 3 3,31 DLL Injection 60% 30% 2 3,41 GPThreats: Fully-automated AI-generated malware and its security risks 29 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Skeleton Debugger Identification CPUID Check Delete File Delete Itself Set AutoRun XOR String Inject DLL XOR String Load File Decode Base64 Run Memory Exit Start Figure: Malware Variants Skeleton. Building blocks are generated by GPT-3. GPThreats: Fully-automated AI-generated malware and its security risks 30 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Detection Results 0 10 20 30 40 Detecting AVs (#) 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 Samples (#) Detecting AVs for Malware Variants Figure: Malware variants detection rates vary according to the functions used to implement the same behaviors. GPThreats: Fully-automated AI-generated malware and its security risks 31 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion A Malicious CoPilot Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 32 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion A Malicious CoPilot GPT-3 vs. CoPilot Behavior Compilable Functional First Time (s) GPT-3 CoPilot GPT-3 CoPilot GPT-3 CoPilot GPT-3 CoPilot String XORing 88% 80% 70% 100% -/4 1/1 2,49 44s/9s Debugger Identification 84% 20% 10% 63% -/2 2/2 2,63 44s/9s Remove File 95% 60% 90% 92% -/2 1/1 2,17 44s/9s Payload Loading 91% 100% 40% 23% -/2 1/2 3,21 44s/9s CPUID check 83% 40% 30% 51% -/2 3/3 3,45 44s/9s Delete Itself 94% 80% 40% 76% -/3 1/1 2,36 44s/9s Memory Run 60% 100% 20% 51% -/2 2/2 2,11 44s/9s AutoRun 99% 80% 20% 17% -/5 2/3 2,41 44s/9s Base64 60% 20% 10% 14% -/3 1/2 3,31 44s/9s DLL Injection 60% 100% 30% 4% -/2 1/5 3,41 44s/9s Watch it: https://youtu.be/6P92ayn2qt0?si=ONHIFKuJLup6rUyY&t=37 GPThreats: Fully-automated AI-generated malware and its security risks 33 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 34 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Adversarial Examples: GANs Malware Noise Generator Black-Box Detector Goodware Discriminator Figure: Generative Adversarial Networks GPThreats: Fully-automated AI-generated malware and its security risks 35 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Adversarial Examples: GANs + LLMs Prompt LLM Generator Malware GAN Generator Prompt LLM Generator Malware Figure: GANs + LLMs GPThreats: Fully-automated AI-generated malware and its security risks 36 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Evading real AVs Table: AV Detection (#) vs. GAN Iterations. Iteration 0 Iteration 1 Iteration 2 GAN1 48 48 (-0%) 47 (-2.08%) GAN2 56 55 (-1.78%) 55 (-0%) GAN3 54 53 (-1.85%) 46 (-14.81%) GPThreats: Fully-automated AI-generated malware and its security risks 37 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Evading real AVs 5 0 5 20 0 20 AVs (#) GAN 1 (Iteration 1) 5 0 5 20 0 20 GAN 1 (Iteration 2) 5 0 5 20 0 20 AVs (#) 5 0 5 20 0 20 5 0 5 Samples (x10K) 20 0 20 AVs (#) 5 0 5 Samples (x10K) 20 0 20 AV Detection: GAN Effect vs. Iterations Figure: AV Detection rates. (In/De)crease vs. GANs. GPThreats: Fully-automated AI-generated malware and its security risks 38 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 39 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware What else can we do beyond writing new code? Teaching LLMs to obfuscate malware GPThreats: Fully-automated AI-generated malware and its security risks 40 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Obfuscating Existing Malware 1 // Consider the following code: 2 void foo(){ cout << "string" << endl; 3 // Modified to the following: 4 void foo(){ cout << DEC(ENC("string",KEY),KEY) << endl; 5 // Do the same to the following code: 6 void bar(){ cout <<< "another␣string" << endl; 7 // result 8 void nar(){ cout << DEC(ENC("another␣string",KEY),KEY) << endl; Code 7: Teaching the model to obfuscate strings. GPThreats: Fully-automated AI-generated malware and its security risks 41 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Obfuscating Existing Malware Table: Obfuscation Effect. Strings obfuscation impacts AV detection more than binary packing. Malware Plain Packed Strings Strings+Pack Alina 52/70 50/70 43/70 43/70 Dexter 38/70 37/70 35/70 37/70 Trochilus 27/70 24/70 24/70 24/70 GPThreats: Fully-automated AI-generated malware and its security risks 42 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 43 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Can we defend using the same arms? Teaching LLMs to deobfuscate code GPThreats: Fully-automated AI-generated malware and its security risks 44 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 var _$_029 ..42=["x67x65x74 ...","x41x42x43 ... x7a","x72 x61 ... x68"]; 2 function CabDorteFidxteFPs (l){ 3 var m= new Date (); var j=0; 4 while(j< (l* 1000)){ 5 var k= new Date (); 6 var j=k[_$_029 ...42[0]]() - m[_$_029 ...42[0]]() Code 8: Obfuscated JS code. Real malware. GPThreats: Fully-automated AI-generated malware and its security risks 45 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // Rename the array variable to _mapping all over the code 2 var _mapping =["x67x65x74 ...","x41x42x43 ... x7a","x72 x61 ... x68"]; 3 function CabDorteFidxteFPs (l){ 4 var m= new Date (); var j=0; 5 while(j< (l* 1000)){ 6 var k= new Date (); 7 var j=k[_mapping [0]]() - m[_mapping [0]]() Code 9: JS Deobfuscation. Variable Renaming. GPThreats: Fully-automated AI-generated malware and its security risks 46 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // Convert array bytes to readable chars 2 var _mapping =["getTime",," ABCDEFGHIJKLMNOPQRSTUVWXYZ ... 3 .... abcdefghijklmnopqrstuvwxyz ","random","length"]; 4 function CabDorteFidxteFPs (l){ 5 var m= new Date (); var j=0; 6 while(j< (l* 1000)){ 7 var k= new Date (); 8 var j=k[_mapping [0]]() - m[_mapping [0]]() Code 10: JS Deobfuscation. String Encoding. GPThreats: Fully-automated AI-generated malware and its security risks 47 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // For the function , replace accesses to _mapping[index] by the array element corresponding to that index. 2 var _mapping =["getTime"," ABCDEFGHIJKLMNOPQRSTUVWXYZ ... 3 abcdefghijklmnopqrstuvwxyz ","random","length"]; 4 function CabDorteFidxteFPs (l){ 5 var m= new Date (); var j=0; 6 while(j< (l* 1000)){ 7 var k= new Date (); 8 var j=k["getTime"]()- m["getTime"]() Code 11: JS Deobfuscation. Array Dereferencing. GPThreats: Fully-automated AI-generated malware and its security risks 48 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Isn’t there a way to detect the automatically-created samples? Exploiting binary similarity for malware detection GPThreats: Fully-automated AI-generated malware and its security risks 49 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Samples Similarity 0 100 200 300 400 500 600 700 800 Samples (#) 1 2 3 4 5 6 7 8 9 10 11 Cluster Size (#) Cluster Size Distribution (Similarity=100) Figure: Malware Variants Similarity. Identified via LSH scores. GPThreats: Fully-automated AI-generated malware and its security risks 50 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 51 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Last but not least! Education: A course on GPT for Security GPThreats: Fully-automated AI-generated malware and its security risks 52 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Course GPThreats: Fully-automated AI-generated malware and its security risks 53 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 54 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 55 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 56 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 57 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 58 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Summary About LLMs We are impressed by the tip of the iceberg!: Most libraries are not fully supported, but we can still do amazing stuff with what is supported. Do not confuse bootstraping with fully automation!: Most code still fail to compile, but they are natural polymorphic code generators when they work. To the infinity and beyond!: If prompts are blocked, one finds a bypass. If no API is provided, one builds an API. Hackers gonna hack. About malware creation Divide and Conquer!: Split tasks in building blocks. Meta-Generators!: Use a GAN to write the LLM prompts. GPThreats: Fully-automated AI-generated malware and its security risks 59 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Summary The security implications: Don’t Panic! It is not as simple as just asking ChatGPT. Also don’t overlook! Attackers can generate millions of samples. Long-tail attacks are the problem! Most code does not work, but one out of thousands will be evasive enough. How to move forward: Exploit LLM weaknesses: Similarity Detection. Fight with the same arms!: LLM-based defenses. Education: LLM-focused awareness. GPThreats: Fully-automated AI-generated malware and its security risks 60 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Why don’t you try yourself? GPThreats: Fully-automated AI-generated malware and its security risks 61 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Check it out! Figure: https://github.com/marcusbotacin/Automated.Malware.Generation GPThreats: Fully-automated AI-generated malware and its security risks 62 / 63 HOU.SEC.CON 2024
Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Thanks! Questions? Comments? botacin@tamu.edu @MarcusBotacin GPThreats: Fully-automated AI-generated malware and its security risks 63 / 63 HOU.SEC.CON 2024
GPThreats: Fully-automated AI-generated malware and its security risks

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GPThreats: Fully-automated AI-generated malware and its security risks

  • 2. Introduction The first attack A newer attack Moving Forward Conclusion Whoami Education Assistant Professor @ TAMU (Since 2022) CS PhD @ UFPR, Brazil (2021) CSE/ECE BSc. + CS MSC @ UNICAMP, Brazil (2015, 2017) Research Malware at high-level: ML-based detectors. Malware at mid-level: Sandboxes and tracers. Malware at low-level: HW-based detectors. Current Project NSF SaTC: Hardware Performance Counters as the next-gen AVs. GPThreats: Fully-automated AI-generated malware and its security risks 2 / 63 HOU.SEC.CON 2024
  • 3. Introduction The first attack A newer attack Moving Forward Conclusion Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 3 / 63 HOU.SEC.CON 2024
  • 4. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 4 / 63 HOU.SEC.CON 2024
  • 5. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence GPT-3: Threats Figure: Source: https://research.nccgroup.com/2021/12/31/on-the-malicious-use- of-large-language-models-like-gpt-3/ GPThreats: Fully-automated AI-generated malware and its security risks 5 / 63 HOU.SEC.CON 2024
  • 6. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Is it a real threat? GPThreats: Fully-automated AI-generated malware and its security risks 6 / 63 HOU.SEC.CON 2024
  • 7. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence GPT-3: Threats Figure: Source: https://research.checkpoint.com/2023/o pwnai-cybercriminals-starting-to-use-chatgpt/ GPThreats: Fully-automated AI-generated malware and its security risks 7 / 63 HOU.SEC.CON 2024
  • 8. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence How would attackers use LLMs? GPThreats: Fully-automated AI-generated malware and its security risks 8 / 63 HOU.SEC.CON 2024
  • 9. Introduction The first attack A newer attack Moving Forward Conclusion GPTs Emergence Exploit Kits GPThreats: Fully-automated AI-generated malware and its security risks 9 / 63 HOU.SEC.CON 2024
  • 10. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 10 / 63 HOU.SEC.CON 2024
  • 11. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware ChatGPT: Prompt Protection GPThreats: Fully-automated AI-generated malware and its security risks 11 / 63 HOU.SEC.CON 2024
  • 12. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware GPT-3: Playground Figure: Source: https://platform.openai.com/playground GPThreats: Fully-automated AI-generated malware and its security risks 12 / 63 HOU.SEC.CON 2024
  • 13. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware GPT-3: API Figure: Source: https://github.com/openai/openai-python GPThreats: Fully-automated AI-generated malware and its security risks 13 / 63 HOU.SEC.CON 2024
  • 14. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Playground: Textual Issues GPThreats: Fully-automated AI-generated malware and its security risks 14 / 63 HOU.SEC.CON 2024
  • 15. Introduction The first attack A newer attack Moving Forward Conclusion Attempts to write malware Playground: Coding issues GPThreats: Fully-automated AI-generated malware and its security risks 15 / 63 HOU.SEC.CON 2024
  • 16. Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 16 / 63 HOU.SEC.CON 2024
  • 17. Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Supported Functions libeay32.dll mtxoci.dll nddeapi.dll shfolder.dll glu32.dll pstorec.dll borlndmm.dll hid.dll libcurl.dll ddraw.dll authz.dll imm32.dll libxml2.dll duilib.dll libusb-1.0.dll ws2_32.dll gdiplus.dll wtsapi32.dll pdh.dll opengl32.dll winhttp.dll mpr.dll activeds.dll vdmdbg.dll dnsapi.dll esent.dll icmp.dll mapi32.dll msvcrt.dll kernel32.dll msacm32.dll version.dll user32.dll rpcrt4.dll winsta.dll advapi32.dll setupapi.dll avifil32.dll cryptui.dll dbghelp.dll uxtheme.dll gdi32.dll wininet.dll winmm.dll iphlpapi.dll shell32.dll samlib.dll crypt32.dll ntdll.dll psapi.dll winscard.dll fltlib.dll credui.dll wsock32.dll winspool.drv netapi32.dll comctl32.dll rasapi32.dll oleaut32.dll jli.dll wintrust.dll shlwapi.dll userenv.dll ole32.dll usp10.dll util.dll comdlg32.dll dllg2.dll msvbvm60.dll oleacc.dll ntoskrnl.exe mobsync.dll imagehlp.dll nvcuda.dll secur32.dll mprapi.dll wbemcomn.dll cmutil.dll msvcr120.dll Libraries 0 10 20 30 40 50 60 70 80 90 100 Supported Functions (%) Library Support Measurement Figure: Supported functions vs. libraries. Some libraries present more functions supported by GPT-3 than others. GPThreats: Fully-automated AI-generated malware and its security risks 17 / 63 HOU.SEC.CON 2024
  • 18. Introduction The first attack A newer attack Moving Forward Conclusion Windows API Support Function Support vs. Popularity 0 10 20 30 40 50 60 70 80 90 100 Sample Frequency (%) Supported Not Supported Rarely-Used Frequentely-Used Figure: Function support vs. prevalence. There is a reasonable number of GPT-3-supported frequently used functions. GPThreats: Fully-automated AI-generated malware and its security risks 18 / 63 HOU.SEC.CON 2024
  • 19. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 19 / 63 HOU.SEC.CON 2024
  • 20. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Building Blocks Table: Supported Functions and Malicious Behaviors. Id Functions (tuple) Subsystem Malicious Use Behavior Name Behavior Class API LoCs 1 OpenFile FileSystem Load payload from file Payload Execution 2 12 ReadFile Loading CloseFile 2 IsDebuggerPresent Utils Check if not running Debugger Targeting 1 5 AdjustTokenPrivileges Security in an analysis environment Identification SetWindowsHookEx Data Acquisition before being malicious 3 OpenFile FileSystem Delete a referenced file Remove File Evidence 1 5 DeleteFile Removal CreateFile 4 DeleteFile FileSystem Remove own binary Delete Itself Evidence 2 10 GetFileSize FileSystem Removal GetModuleName Process 5 RegSetValueKeyExA Registry Set its own path AutoRun Persistence 4 28 GetModuleFilePath Process in the AutoRun entry RegOpenKeyA Registry GPThreats: Fully-automated AI-generated malware and its security risks 20 / 63 HOU.SEC.CON 2024
  • 21. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Building Blocks Table: Supported Functions and Malicious Behaviors. Id Functions (tuple) Subsystem Malicious Use Behavior Name Behavior Class API LoCs 6 CryptBinarytoStringA Utils Decode payload Base64 Obfuscation 4 12 URLDownloadToFile Network retrieved from the Internet WriteFile FileSystem saving to a file 7 VirtualAlloc Memory Write a payload DLL Injection Injection 12 37 WriteProcessMemory Memory in another process CreateRemoteThread Process memory space 8 VirtualProtect Memory Set page permission Memory Run Arbitrary 2 6 CreateMutex Synchronization to run a payload Execution CloseFile FileSystem directly from memory 9 N/A N/A encode a string using XOR String XORing Obfuscation 0 10 10 N/A N/A Check CPU model via CPUID CPUID check Targeting 2 9 GPThreats: Fully-automated AI-generated malware and its security risks 21 / 63 HOU.SEC.CON 2024
  • 22. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Is creating building blocks straightforward? The Challenges GPThreats: Fully-automated AI-generated malware and its security risks 22 / 63 HOU.SEC.CON 2024
  • 23. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Instructing Building Blocks Creation Table: Model Commands. Commands given to the model to avoid frequent model biases. Command Goal Put in a function Avoid coding in the main Code for Windows Avoid coding for Linux Function in C Avoid producing javascript Use the Windows API Avoid using C++ internals Use the prototype f() Facilitate Integration GPThreats: Fully-automated AI-generated malware and its security risks 23 / 63 HOU.SEC.CON 2024
  • 24. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Instructing Building Blocks Creation Table: Systematic Errors. Undesired constructions that can be easily fixed by the attackers. Error Fix ASCII vs. UNICODE Replace A and W Missing headers Add fixed set of headers C vs. C++ print using cout vs printf Missing definitions Pre-defined definitions Explicit casts Disable Warnings Excessive prints Statement removal GPThreats: Fully-automated AI-generated malware and its security risks 24 / 63 HOU.SEC.CON 2024
  • 25. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Building Blocks Examples 1 // Code in C 2 // Code for Windows 3 // Use the Windows API 4 // Write a function 5 // Function prototype is: BOOL CPU_Identification (); 6 // Identifies if running under an Intel CPU or not Code 1: CPU Identification Prompt. GPThreats: Fully-automated AI-generated malware and its security risks 25 / 63 HOU.SEC.CON 2024
  • 26. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 BOOL CPU_Identification (){ 2 int CPUInfo [4]; 3 __cpuid(CPUInfo , 0); 4 if (! strcmp (( char *)&CPUInfo [1], "uneG")) 5 return 1; Code 2: CPU identification via CPUID. 1 BOOL CPU_Identification () { 2 if ( IsProcessorFeaturePresent ( PF_XMMI64_INSTRUCTIONS_AVAILABLE )) 3 return TRUE; Code 3: CPU identification via processor feature. GPThreats: Fully-automated AI-generated malware and its security risks 26 / 63 HOU.SEC.CON 2024
  • 27. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 // Code in C 2 // Code for Windows 3 // Use the Windows API 4 // Write a function 5 // Function prototype is: BOOL Debugger_Identification (); 6 // Identifies if running under a debugger or not Code 4: Debugger Identification Prompt. GPThreats: Fully-automated AI-generated malware and its security risks 27 / 63 HOU.SEC.CON 2024
  • 28. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Multiple Implementations of the same Building Block 1 bool Debugger_Identification () { 2 PEB peb; 3 __asm { mov eax , fs: [0x30]; 4 mov peb , eax; } 5 return (peb.BeingDebugged == 1) ? true : false; Code 5: Debugger detection in 32-bit systems. 1 bool Debugger_Identification () { 2 PEB peb; 3 __asm { mov rax , gs: [0x60]; 4 mov peb , rax; } 5 return (peb.BeingDebugged == 1) ? true : false; Code 6: Debugger detection in 64-bit systems. GPThreats: Fully-automated AI-generated malware and its security risks 28 / 63 HOU.SEC.CON 2024
  • 29. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Samples Creation & Functionality Testing Table: Building Block Generation. Compilation and Sandboxing success rates, first occurence of a functional code, and code generation time. Behavior Compilable Functional First Time (s) String XORing 88% 70% 4 2,49 Debugger Identification 84% 10% 2 2,63 Remove File 95% 90% 2 2,17 Payload Loading 91% 40% 2 3,21 CPUID check 83% 30% 2 3,45 Delete Itself 94% 40% 3 2,36 Memory Run 60% 20% 2 2,11 AutoRun 99% 20% 5 2,41 Base64 60% 10% 3 3,31 DLL Injection 60% 30% 2 3,41 GPThreats: Fully-automated AI-generated malware and its security risks 29 / 63 HOU.SEC.CON 2024
  • 30. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Malware Skeleton Debugger Identification CPUID Check Delete File Delete Itself Set AutoRun XOR String Inject DLL XOR String Load File Decode Base64 Run Memory Exit Start Figure: Malware Variants Skeleton. Building blocks are generated by GPT-3. GPThreats: Fully-automated AI-generated malware and its security risks 30 / 63 HOU.SEC.CON 2024
  • 31. Introduction The first attack A newer attack Moving Forward Conclusion Building Blocks Detection Results 0 10 20 30 40 Detecting AVs (#) 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 Samples (#) Detecting AVs for Malware Variants Figure: Malware variants detection rates vary according to the functions used to implement the same behaviors. GPThreats: Fully-automated AI-generated malware and its security risks 31 / 63 HOU.SEC.CON 2024
  • 32. Introduction The first attack A newer attack Moving Forward Conclusion A Malicious CoPilot Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 32 / 63 HOU.SEC.CON 2024
  • 33. Introduction The first attack A newer attack Moving Forward Conclusion A Malicious CoPilot GPT-3 vs. CoPilot Behavior Compilable Functional First Time (s) GPT-3 CoPilot GPT-3 CoPilot GPT-3 CoPilot GPT-3 CoPilot String XORing 88% 80% 70% 100% -/4 1/1 2,49 44s/9s Debugger Identification 84% 20% 10% 63% -/2 2/2 2,63 44s/9s Remove File 95% 60% 90% 92% -/2 1/1 2,17 44s/9s Payload Loading 91% 100% 40% 23% -/2 1/2 3,21 44s/9s CPUID check 83% 40% 30% 51% -/2 3/3 3,45 44s/9s Delete Itself 94% 80% 40% 76% -/3 1/1 2,36 44s/9s Memory Run 60% 100% 20% 51% -/2 2/2 2,11 44s/9s AutoRun 99% 80% 20% 17% -/5 2/3 2,41 44s/9s Base64 60% 20% 10% 14% -/3 1/2 3,31 44s/9s DLL Injection 60% 100% 30% 4% -/2 1/5 3,41 44s/9s Watch it: https://youtu.be/6P92ayn2qt0?si=ONHIFKuJLup6rUyY&t=37 GPThreats: Fully-automated AI-generated malware and its security risks 33 / 63 HOU.SEC.CON 2024
  • 34. Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 34 / 63 HOU.SEC.CON 2024
  • 35. Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Adversarial Examples: GANs Malware Noise Generator Black-Box Detector Goodware Discriminator Figure: Generative Adversarial Networks GPThreats: Fully-automated AI-generated malware and its security risks 35 / 63 HOU.SEC.CON 2024
  • 36. Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Adversarial Examples: GANs + LLMs Prompt LLM Generator Malware GAN Generator Prompt LLM Generator Malware Figure: GANs + LLMs GPThreats: Fully-automated AI-generated malware and its security risks 36 / 63 HOU.SEC.CON 2024
  • 37. Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Evading real AVs Table: AV Detection (#) vs. GAN Iterations. Iteration 0 Iteration 1 Iteration 2 GAN1 48 48 (-0%) 47 (-2.08%) GAN2 56 55 (-1.78%) 55 (-0%) GAN3 54 53 (-1.85%) 46 (-14.81%) GPThreats: Fully-automated AI-generated malware and its security risks 37 / 63 HOU.SEC.CON 2024
  • 38. Introduction The first attack A newer attack Moving Forward Conclusion Automatic Evasive Prompts Evading real AVs 5 0 5 20 0 20 AVs (#) GAN 1 (Iteration 1) 5 0 5 20 0 20 GAN 1 (Iteration 2) 5 0 5 20 0 20 AVs (#) 5 0 5 20 0 20 5 0 5 Samples (x10K) 20 0 20 AVs (#) 5 0 5 Samples (x10K) 20 0 20 AV Detection: GAN Effect vs. Iterations Figure: AV Detection rates. (In/De)crease vs. GANs. GPThreats: Fully-automated AI-generated malware and its security risks 38 / 63 HOU.SEC.CON 2024
  • 39. Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 39 / 63 HOU.SEC.CON 2024
  • 40. Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware What else can we do beyond writing new code? Teaching LLMs to obfuscate malware GPThreats: Fully-automated AI-generated malware and its security risks 40 / 63 HOU.SEC.CON 2024
  • 41. Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Obfuscating Existing Malware 1 // Consider the following code: 2 void foo(){ cout << "string" << endl; 3 // Modified to the following: 4 void foo(){ cout << DEC(ENC("string",KEY),KEY) << endl; 5 // Do the same to the following code: 6 void bar(){ cout <<< "another␣string" << endl; 7 // result 8 void nar(){ cout << DEC(ENC("another␣string",KEY),KEY) << endl; Code 7: Teaching the model to obfuscate strings. GPThreats: Fully-automated AI-generated malware and its security risks 41 / 63 HOU.SEC.CON 2024
  • 42. Introduction The first attack A newer attack Moving Forward Conclusion Armoring Existing Malware Obfuscating Existing Malware Table: Obfuscation Effect. Strings obfuscation impacts AV detection more than binary packing. Malware Plain Packed Strings Strings+Pack Alina 52/70 50/70 43/70 43/70 Dexter 38/70 37/70 35/70 37/70 Trochilus 27/70 24/70 24/70 24/70 GPThreats: Fully-automated AI-generated malware and its security risks 42 / 63 HOU.SEC.CON 2024
  • 43. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 43 / 63 HOU.SEC.CON 2024
  • 44. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Can we defend using the same arms? Teaching LLMs to deobfuscate code GPThreats: Fully-automated AI-generated malware and its security risks 44 / 63 HOU.SEC.CON 2024
  • 45. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 var _$_029 ..42=["x67x65x74 ...","x41x42x43 ... x7a","x72 x61 ... x68"]; 2 function CabDorteFidxteFPs (l){ 3 var m= new Date (); var j=0; 4 while(j< (l* 1000)){ 5 var k= new Date (); 6 var j=k[_$_029 ...42[0]]() - m[_$_029 ...42[0]]() Code 8: Obfuscated JS code. Real malware. GPThreats: Fully-automated AI-generated malware and its security risks 45 / 63 HOU.SEC.CON 2024
  • 46. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // Rename the array variable to _mapping all over the code 2 var _mapping =["x67x65x74 ...","x41x42x43 ... x7a","x72 x61 ... x68"]; 3 function CabDorteFidxteFPs (l){ 4 var m= new Date (); var j=0; 5 while(j< (l* 1000)){ 6 var k= new Date (); 7 var j=k[_mapping [0]]() - m[_mapping [0]]() Code 9: JS Deobfuscation. Variable Renaming. GPThreats: Fully-automated AI-generated malware and its security risks 46 / 63 HOU.SEC.CON 2024
  • 47. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // Convert array bytes to readable chars 2 var _mapping =["getTime",," ABCDEFGHIJKLMNOPQRSTUVWXYZ ... 3 .... abcdefghijklmnopqrstuvwxyz ","random","length"]; 4 function CabDorteFidxteFPs (l){ 5 var m= new Date (); var j=0; 6 while(j< (l* 1000)){ 7 var k= new Date (); 8 var j=k[_mapping [0]]() - m[_mapping [0]]() Code 10: JS Deobfuscation. String Encoding. GPThreats: Fully-automated AI-generated malware and its security risks 47 / 63 HOU.SEC.CON 2024
  • 48. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Deobfuscating Real Malware 1 // For the function , replace accesses to _mapping[index] by the array element corresponding to that index. 2 var _mapping =["getTime"," ABCDEFGHIJKLMNOPQRSTUVWXYZ ... 3 abcdefghijklmnopqrstuvwxyz ","random","length"]; 4 function CabDorteFidxteFPs (l){ 5 var m= new Date (); var j=0; 6 while(j< (l* 1000)){ 7 var k= new Date (); 8 var j=k["getTime"]()- m["getTime"]() Code 11: JS Deobfuscation. Array Dereferencing. GPThreats: Fully-automated AI-generated malware and its security risks 48 / 63 HOU.SEC.CON 2024
  • 49. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Isn’t there a way to detect the automatically-created samples? Exploiting binary similarity for malware detection GPThreats: Fully-automated AI-generated malware and its security risks 49 / 63 HOU.SEC.CON 2024
  • 50. Introduction The first attack A newer attack Moving Forward Conclusion Defenders Perspective Samples Similarity 0 100 200 300 400 500 600 700 800 Samples (#) 1 2 3 4 5 6 7 8 9 10 11 Cluster Size (#) Cluster Size Distribution (Similarity=100) Figure: Malware Variants Similarity. Identified via LSH scores. GPThreats: Fully-automated AI-generated malware and its security risks 50 / 63 HOU.SEC.CON 2024
  • 51. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 51 / 63 HOU.SEC.CON 2024
  • 52. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Last but not least! Education: A course on GPT for Security GPThreats: Fully-automated AI-generated malware and its security risks 52 / 63 HOU.SEC.CON 2024
  • 53. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead Course GPThreats: Fully-automated AI-generated malware and its security risks 53 / 63 HOU.SEC.CON 2024
  • 54. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 54 / 63 HOU.SEC.CON 2024
  • 55. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 55 / 63 HOU.SEC.CON 2024
  • 56. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 56 / 63 HOU.SEC.CON 2024
  • 57. Introduction The first attack A newer attack Moving Forward Conclusion Stepping Ahead ChatGPT Fun GPThreats: Fully-automated AI-generated malware and its security risks 57 / 63 HOU.SEC.CON 2024
  • 58. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Agenda 1 Introduction GPTs Emergence Attempts to write malware 2 The first attack Windows API Support Building Blocks 3 A newer attack A Malicious CoPilot Automatic Evasive Prompts 4 Moving Forward Armoring Existing Malware Defenders Perspective 5 Conclusion Stepping Ahead Final Remarks GPThreats: Fully-automated AI-generated malware and its security risks 58 / 63 HOU.SEC.CON 2024
  • 59. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Summary About LLMs We are impressed by the tip of the iceberg!: Most libraries are not fully supported, but we can still do amazing stuff with what is supported. Do not confuse bootstraping with fully automation!: Most code still fail to compile, but they are natural polymorphic code generators when they work. To the infinity and beyond!: If prompts are blocked, one finds a bypass. If no API is provided, one builds an API. Hackers gonna hack. About malware creation Divide and Conquer!: Split tasks in building blocks. Meta-Generators!: Use a GAN to write the LLM prompts. GPThreats: Fully-automated AI-generated malware and its security risks 59 / 63 HOU.SEC.CON 2024
  • 60. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Summary The security implications: Don’t Panic! It is not as simple as just asking ChatGPT. Also don’t overlook! Attackers can generate millions of samples. Long-tail attacks are the problem! Most code does not work, but one out of thousands will be evasive enough. How to move forward: Exploit LLM weaknesses: Similarity Detection. Fight with the same arms!: LLM-based defenses. Education: LLM-focused awareness. GPThreats: Fully-automated AI-generated malware and its security risks 60 / 63 HOU.SEC.CON 2024
  • 61. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Why don’t you try yourself? GPThreats: Fully-automated AI-generated malware and its security risks 61 / 63 HOU.SEC.CON 2024
  • 62. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Check it out! Figure: https://github.com/marcusbotacin/Automated.Malware.Generation GPThreats: Fully-automated AI-generated malware and its security risks 62 / 63 HOU.SEC.CON 2024
  • 63. Introduction The first attack A newer attack Moving Forward Conclusion Final Remarks Thanks! Questions? Comments? botacin@tamu.edu @MarcusBotacin GPThreats: Fully-automated AI-generated malware and its security risks 63 / 63 HOU.SEC.CON 2024