Putting the dynamic into software security testing

Transcription

1 Ptting the dynamic into software secrity testing Detecting and Addressing Cybersecrity Isses V

2 Code ahead! 2

3 Atomated vlnerability detection and triage + = 3

4 How did we get here? Vector was engaged with a large, US Tier 1 and we were addressing software qality They acknowledged they had software qality isses Concern was related to how these qality isses cold affect secrity Project goals morphed into low-hanging secrity frit (for both the cstomer and the attacker)! Or goal was more along the lines of robstness! 4

5 VectorCAST test atomation platform 5

6 Vector testing soltion Software System Link to Reqirements System Validation System Integration Test Software Integration Test Software Unit Test System validation + code coverage on ECU Change-Based Testing SW integration testing + code coverage on PC White-box testing on host / on target CANoe, vteststdio VT System VectorCAST/QA CANoe, vteststdio vvirtualtarget VectorCAST/C++ and /QA VectorCAST/C++ VectorCAST Analytics Software Implementation Benefits Fll spport in the development process, from software nit test to system validation Uniform test management, test atomation (CI), reslt analysis and traceability 6

7 Vlnerability detection via dynamic analysis The idea The approach To be able to identify and atomatically test for ndiagnosed secrity vlnerabilities Utilizes MITRE s classification of CWEs (common weakness enmeration) Once an instance of a generic CWE is fond in the software, that isse is then classed as a CVE (common vlnerability and exposre) Atomatically interrogate the code and identify possible weaknesses (a la static analysis) Once a potential CWE is fond, generate a test exploiting the identified isse and execte it (dynamic exection) After exection, analyses the exection trace and decide if the potential CWE is a genine threat Code CWEs Tests Exection Analysis CVEs 7

8 Vlnerability detection via dynamic analysis The benefits Weaknesses identified Unlike static analysis, this method will only flag an isse if we can generate an exploit, eliminating the false-positive isse plaging static analysis The generation of test artefacts allows for their ftre re-exection to demonstrate the mitigation of a potential isse after software redesign Can be sed for both on-host and on-target exection (think secrity validation for embedded systems) Via the analysis of open-sorce projects, a nmber of API-sage related isses have been identified A large US atomotive Tier 1 has sed it to find secrity-specific rese isses on their software platform Able to atomatically find isses sch as NULL pointer dereference (CWE-476), classic bffer overflow (CWE-120) and improper resorce shtdown/release (CWE-404) Atomated Validation 8

9 Two technical approaches Mtational (test-site) fzz testing Take an existing test-site Modify the vales to be randomly erroneos Rn it with coverage Does it crash? If yes: potential weakness! Directed ( intelligent ) secrity testing Identify an expression of interest > E.g., pointer dereference, divide by zero Generate a test reaching that line with erroneos vales Rn it with coverage Does it crash? If yes: potential weakness! 9

10 Example from lighttpd int bffer_copy_string_bffer(bffer *b, const bffer *src) { if (!src) retrn -1; } if (src->sed == 0) { b->sed = 0; retrn 0; } retrn bffer_copy_string_len(b, src->ptr, src->sed - 1); Not detected: CppCheck, Facebook s Infer, Uno Possible error: Lint, CodeHawk Programmatic error detected (SIGSEGV): VectorCAST 10

11 Secrity weaknesses of interest The approach is focsed on atomatically generating tests for a nmber of classifications of vlnerabilities according to MITRE At the highest level, we look to address the general banner CWE-398 ( indication of poor code qality ) Some examples of isses we aim to detect Hard errors Use of a NULL pointer (CWE-476) Bffer {nder,over}flow (stack corrption) (CWE-124) Divide by zero (CWE-369) Mismatched calls malloc/free, fopen/fclose, pthread_mtex_lock/pthread_mtex_nlock (CWE- 401/404/413/415/590) Bad argments memcpy (CWE-120/130) Unchecked retrn malloc (CWE-252/690) 11

12 12 Technical Approach

13 Atomated (mtational) fzz testing for nit testing Existing test-case: > TEST.VALUE:bffer.bffer_copy_string_bffer.src:<<malloc 1>> > TEST.VALUE:bffer.bffer_copy_string_bffer.src[0].sed:0 > TEST.VALUE:bffer.bffer_copy_string_bffer.b:<<malloc 1>> Maniplate the vales: > TEST.VALUE:bffer.bffer_copy_string_bffer.src:<<malloc 1>> > TEST.VALUE:bffer.bffer_copy_string_bffer.src[0].sed:0 > TEST.VALUE:bffer.bffer_copy_string_bffer.b:<<nll>> Execte! 13

14 From software to mathematics Replace x with code and 0 with nll pointer dereference 14

15 Directed test-case generation for weaknesses We combine in-depth static analysis with constraint solving to identify more complex weaknesses: > param_2->x += 3; > param_3->y += 2; > retrn param_1->z / (param_2->x - param_3->y); Fzz testing has to get lcky here, bt sing test-case generation we can directly generate a test sch that: > (param_2->x 3) (param_3->y 2) 0 This gets fed to a black box oracle that can provide the answer! 15

16 16 Real World Examples

17 Real examples fond (divide by zero) atomotive extern VEHICLE_T Vehicle; void check_speed(int8_t speed_thomph) { int32_t temp,tho_mph,tho_rpm; if(vehicle.wheelspeed<150) {} else { if(speed_thomph>1000) {} else { Tho_MPH=0; } } if(tho_mph==0) { // no change to Tho_MPH! } else {} There exists a nmber of paths throgh the code where Tho_MPH is nassigned (so ndefined behavior) or is assigned to zero What is srprising is that Tho_MPH is checked against 0 and then sed in a divide at the same scope-level No corrective action taken, even thogh the corrective condition is already detected! 17 } temp=(100*tho_rpm)/tho_mph;

18 Real examples fond (NULL pointer) medical STATUS process_lamp_event(lamp_pattern_t patternid, LAMP_TASK_DATA_t *ptrtaskdata) { DRV_RET_CODE_t drvretcode = DRV_RC_ERROR; STATUS retcode = OK; LAMP_PATTERN_t tmppatternid = patternid; A lot of the code for provided in this project was extremely jdicios in checking all parameters Their style of coding made this crash stand-ot, as ptrtaskdata is never checked for nll! if (((LAMP_FAST_BLINKING == ptrtaskdata->previospatternid) (LAMP_SLOW_BLINKING == ptrtaskdata->previospatternid)) && (LAMP_PATTERN_NONE == tmppatternid)) { tmppatternid = ptrtaskdata->previospatternid; } } ptrtaskdata->conter += ptrtaskdata->timeot_ms; 18

19 19 Actionable Intelligence

20 Actionable Intelligence Software metrics An approach to ascertaining qickly Chess Morningstar for Software Secrity absence of obvios reliability isses This similar to CWE-398 for poor code qality The easy ones Defect density Defects/SLoC Lines free from obvios isses (via code coverage) Confidence of defect freedom (bt not garanteed!) Ratio of secrity tests free of defects Higher ratio => more secre 20

21 Actionable Intelligence Open Sorce analysis Project Metric LIGHTTPD ZLIB LIBXML2 Version # files SLoC⁶ 36,605 6, ,179 Uniqe # isses ,926 Defect density (defects/line) 1/52 1/60 1/63 Avg. # of tests per defect Tests hitting defects 69% 28% 40% Fnctions with defects 44% 44% 29% Fnctions with vg 20 and defects⁷ 51% 55% 66% ⁶measred with cloc 21 ⁷Jones 08: [complexity] levels greater than 20 are considered hazardos

22 Take home Process Identify portfolio Assess vlnerabilities Manage risk Some of the isses we find yo might consider are non-isses or are mitigated against as part of yor software architectre That s great be wary abot software re-se across projects! Mainly: no one size fits all soltion se mltiple tools! Dynamic exection can find certain vlnerabilities more definitively Need to always consider DP-E ratio (damage potential vs. effort) 22

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