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When all you have is a hammer

We’ve probably all experienced organizations that rely principally on a single assessment technique (whether it be static analysis or dynamic analysis, manual or tool-based). Unfortunately, this is all too common for security practices. When this topic came up recently with the question (paraphrased), “Are there numbers that demonstrate the value of a security program making use of static, dynamic, and manual assessment techniques?” I thought some of our experience might apply.

Synopsys favors delivery of holistic assessments (those assessments that consider architecture/design as well as code, leveraging static & dynamic tools, and using both tool-assisted and manual techniques). During my tenure as Director of Security, we found the following distribution of findings:

  • Static tool: 20%
  • Dynamic tool: 5% (*1)
  • Manual SCR: 15%
  • Architecture Risk Analysis: 60%

Those numbers may surprise and demand some explanation. For instance, though Microsoft at one point reported 50% of their findings were “flaws”, we found 60% (on average, with as much as 70%) of its issues as ‘flaws’. If anything, our classification of ‘flaw’ was less inclusive though, and at the time I looked into this, I concluded that our larger percentage could be due to technique, focus on architecture in methodology (LoE), or level of customer familiarity/maturity with the tech-stack they developed on.

Likewise, our process started with a “white box” triage then shifted to exploratory testing (aka: dynamic) rather than the other way around (which I find common elsewhere) and that order-of-operations probably moved balance from dynamic to static. So, in practice we “found” vulnerabilities statically and then supported those findings with dynamic tests to validate their exploit-ability and impact (though perhaps dissatisfying, static gets the point in our data set here).

Slicing by technique

Our experience made it pretty evident to us: skip assessment techniques and expect to miss some critical vulnerabilities.

Slicing by vulnerability type

As I’ve mentioned in previous blog entries and mailing list posts, security managers need the capability to assess and report on their ability to find particular classes of security vulnerability as well. WASC’s Web Application Security Statistics provide a starting point for both classes of attacks you might expect to find and their probability. There’s no shortage of vulnerability taxonomy work out there, so we’ll leave this topic at this superficial level.

Optimizing a Security Practice

Whether you know what percentage of your reported findings come from each assessment technique, or you’re just starting to build your assessment practice, optimization (measurement, analysis, iterative improvement) should be a goal. To do this we combine our knowledge of the techniques we’re employing and the enumeration of vulnerabilities we want to find and report.

Confined to the context of static analysis, I described the following loop in a previous blog post to do just that:

  • Use a representative sample of your applications to observe a static analysis tool’s performance, not a contrived test suite.
  • Consider the tool’s findings relative to pen-testing findings on the same app.
    • Did new and interesting findings result?
    • Did pen-testing findings found by the static tool provide adequate root-cause analysis and remediation advice to fix problems earlier?
  • How long did it take to on-board an app? How will this scale to your portfolio of apps?
  • How long did it take to triage the results? How will this scale?

The purpose of this trial experimentation was to provide some basic comparison between assessment techniques (though you could easily come up with a more thorough approach). Of course, you’d want to consider findings from the perspective of all techniques, not just static tools.

Internally, we had our assessment lab managers write up a qualitative comparison of assessment techniques we employ, comparing how well each is “suited” to finding vulnerabilities against common vulnerability taxonomies (*2) and came up with the following:

  • ARA: 14%
  • Static tool: 12%
  • Manual SCR: 21%
  • Manual Pen: 21%
  • Dynamic Tool: 12%
  • Sec Testing: 20%

At this point, the reader may be asking why my experience differs from our lab managers’. Specifically, for instance, “why are dynamic techniques over-estimated here as compared to to historical findings rates?” Two reasons (at least) come to mind:

  1. The balance of techniques that constitute our assessments are “moving back” towards dynamic as software-under-test and market forces evolve
  2. There’s a difference between how suited a technique is to discover something and how we choose to find it (I guarantee that in some circumstances this gap represent ‘optimization opportunities’, while in others it represents explicit technology or methodological choices).

The Caveat You’ve Come to Expect From Me

Whether your considering a vendor’s assessment service, an assessment standard, or your own internal assessment practice, it’s key to be able to detect biases. When others supply you assessment data you’ll likely uncover biases (though it may not be apparent to those reporting initially). Be careful, I doubt you’ll be able to compile, combine, or relate numbers you receive from varying sources for this reason.

If you’re compiling assessment data for the purpose of motivating practice improvement, you may want to either 1) extract universal similarities from the data you get and present those lessons learned to management, or 2) just list the others’ experience separately. This can provide those to whom you’re presenting a compelling view to your perspective on differing assessment approaches.

In fact, many factors will muck up assessment comparison. For instance, when people report a percentage (like I did above), did they mean finding instances, findings, by type, or something else (*3)? What was their methodology, level-of-effort, and how did they conduct assessment. For instance, risk-based assessments produce a very different distribution (with wider variation) than time-boxed techniques, which more frequently drive to an explicit (or worse, implicit) checklist.

One thing universally seems true though: the more assessment techniques a software security practice makes use of, the more types of vulnerabilities it can expect to report.


(*1) – Clarification: our use of dynamic tools is human-assisted (both in terms of crawl and in follow-up with manual effort or leveraging internally developed tools).

(*2) – WASC, OWASP Top 10, CWE Top 25

(*3) – My data is comprised of reported vulnerabilities. Answering questions about what risk-level I reported requires some abstraction as clients differ in their requirements there. Likewise, behavior regarding instances vs. buckets of ‘related vulns’ requires addl. explanation.

John Steven

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John Steven

John Steven

John Steven is a former senior director at Synopsys. His expertise runs the gamut of software security—from threat modeling and architectural risk analysis to static analysis and security testing. He has led the design and development of business-critical production applications for large organizations in a range of industries. After joining Synopsys as a security researcher in 1998, John provided strategic direction and built security groups for many multinational corporations, including Coke, EMC, Qualcomm, Marriott, and FINRA. His keen interest in automation contributed to keeping Synopsys technology at the cutting edge. He has served as co-editor of the Building Security In department of IEEE Security & Privacy magazine and as the leader of the Northern Virginia OWASP chapter. John speaks regularly at conferences and trade shows.

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