A longish post, but this wasn't going to fit into 140 characters. This is an argument pertaining to security metrics, with a statement that using pure vulnerability count-based metrics to talk about an organisation's application (in)security is insufficient, and suggests an alternative approach. Comments welcome.

Current metrics

Metrics and statistics are certainly interesting (none of those are infosec links). Within our industry, Verizon's Data Breach Investigations Report (DBIR) makes a splash each year, and Veracode are also receiving growing recognition for their State of Software Security (SOSS). Both are interesting to read and contain much insight. The DBIR specifically examines and records metrics for breaches, a post-hoc activity that only occurs once a series of vulnerabilities have been found and exploited by ruffians, while the SOSS provides insight into the opposing end of a system's life-cycle by automatically analysing applications before they are put into production (in a perfect world... no doubt they also examine apps that are already in production). Somewhat tangentially, Dr Geer wrote recently about a different metric for measuring the overall state of Cyber Security, we're currently at a 1021.6. Oh noes!

Apart from the two bookends (SOSS and DBIR), other metrics are also published.

From a testing perspective, WhiteHat releases perhaps the most well-known set of metrics for appsec bugs, and in years gone by, Corsaire released statistics covering their customers. Also in 2008, WASC undertook a project to provide metrics with data sourced from a number of companies, however this too has not seen recent activity (last edit on the site was over a year ago). WhiteHat's metrics measure the number of serious vulnerabilities in each site (High, Critical, Urgent) and then slice and dice this based on the vulnerability's classification, the organisation's size, and the vertical within which they lie. WhiteHat is also in the fairly unique position of being able to record remediation times with a higher granularity than appsec firms that engage with customers through projects rather than service contracts. Corsaire's approach was slightly different; they recorded metrics in terms of the classification of the vulnerability, its impact and the year within which the issue was found. Their report contained similar metrics to the WhiteHat report (e.g. % of apps with XSS), but the inclusion of data from multiple years permitted them to extract trends from their data. (No doubt WhiteHat have trending data, however in the last report it was absent). Lastly, WASC's approach is very similar to WhiteHat's, in that a point in time is selected and vulnerability counts according to impact and classification are provided for that point.

Essentially, each of these approaches uses a base metric of vulnerability tallies, which are then viewed from different angles (classification, time-series, impact). While the metrics are collected per-application, they are easily aggregated into organisations.

Drawback to current approaches

Problems with just counting bugs are well known. If I ask you to rate two organisations, the Ostrogoths and the Visigoths, on their effectiveness in developing secure applications, and I tell you that the Ostrogoths have 20 critical vulnerabilities across their applications, while the Visigoths only have 5, without further data it seems that the Visigoths have the lead. However, if we introduce the fact that the Visigoths have a single application in which all 5 issues appear, while the Ostrogoths spread their 20 bugs across 10 applications, then it's not so easy to crow for the Visigoths, who average 5 bugs per application as oppossed to the Ostrogoth's 2. Most reports take this into account, and report on a percentage of applications that exhibit a particular vulnerability (also seen as the probability that a randomly selected application will exhibit that issue). Unfortunately, even taking into account the number of applications is not sufficient; an organisation with 2 brochure-ware sites does not face the same risk as an organisation with 2 transaction-supporting financial applications, and this is where appsec metrics start to fray.

In the extreme edges of ideal metrics, the ability to factor in chains of vulnerabilities that individually present little risk, but combined is greater than the sum of the parts, would be fantastic. This aspect is ignored by most (including us), as a fruitful path isn't clear.

Why count in the first place?

Let's take a step back, and consider why we produce metrics; with the amount of data floating around, it's quite easy to extract information and publish, thereby earning a few PR points. However, are the metrics meaningful? The quick test is to ask whether they support decision making. For example, does it matter that external attackers were present in an overwhelming number incidents recorded in the DBIR? I suspect that this is an easy "yes", since this metric justifies shifting priorities to extend perimeter controls rather than rolling out NAC.

One could just as easily claim that absolute bug counts are irrelevant and that they need to be relative to some other scale; commonly the number of applications an organisation has. However in this case, if the metrics don't provide enough granularity to accurately position your organisation with respect to others that you actually care about, then they're worthless to you in decision making. What drives many of our customers is not where they stand in relation to every other organisation, but specifically their peers and competitors. It's slightly ironic that oftentimes the more metrics released, the less applicable they are to individual companies. As a bank, knowing you're in the top 10% of a sample of banking organisations means something; when you're in the highest 10% of a survey that includes WebGoat clones, the results are much less clear.

In Seven Myths About Information Security Metrics, Dr Hinson raises a number of interesting points about security metrics. They're mostly applicable to security awareness, however they also carry across into other security activities. At least two serve my selfish needs, so I'll quote them here:

Myth 1: Metrics must be “objective” and “tangible”

There is a subtle but important distinction between measuring subjective factors and measuring subjectively. It is relatively easy to measure “tangible” or objective things (the number of virus incidents, or the number of people trained). This normally gives a huge bias towards such metrics in most measurement systems, and a bias against measuring intangible things (such as level of security awareness). In fact, “intangible” or subjective things can be measured objectively, but we need to be reasonably smart about it (e.g., by using interviews,surveys and audits). Given the intangible nature of security awareness, it is definitely worth putting effort into the measurement of subjective factors, rather than relying entirely on easy-to-measure but largely irrelevant objective factors. [G Hinson]

and

Myth 3: We need absolute measurements

For some unfathomable reason, people often assume we need “absolute measures”—height in meters, weight in pounds, etc. This is nonsense!
If I line up the people in your department against a wall, I can easily tell who is tallest, with no rulers in sight. This yet again leads to an unnecessary bias in many measurement systems. In fact, relative values are often more useful than absolute scales, especially to drive improvement. Consider this for instance: “Tell me, on an (arbitrary) scale from one to ten, how security aware are the people in your department are? OK, I'll be back next month to ask you the same question!” We need not define the scale formally, as long as the person being asked (a) has his own mental model of the processes and (b) appreciates the need to improve them. We needn't even worry about minor variations in the scoring scale from month to month, as long as our objective of promoting improvement is met. Benchmarking and best practice transfer are good examples of this kind of thinking. “I don't expect us to be perfect, but I'd like us to be at least as good as standard X or company Y. [G Hinson]

While he writes from the view of an organisation trying to decide whether their security awareness program is yielding dividends, the core statements are applicable for organisations seeking to determine the efficacy of their software security program. I'm particularly drawn by two points: the first is that intangibles are as useful as concrete metrics, and the second is that absolute measurements aren't necessary, comparative ordering is sometimes enough.

Considering cost

It seems that one of the intangibles that currently published appsec metrics don't take into account, is cost to the attacker. No doubt behind each vulnerability's single impact rating are a multitude of factors that contribute, one of which may be something like "Complexity" or "Ease of Exploitation". However, measuring effort in this way is qualitative and only used as a component in the final rating. I'm suggesting that cost (interchangeable with effort) be incorporated into the base metric used when slicing datasets into views. This will allow you to understand the determination an attacker would require when facing one of your applications. Penetration testing companies are in a unique position to provide this estimate; a tester unleashed on an application project is time-bounded and throws their experience and knowledge at the app. At the end, one can start to estimate how much effort was required to produce the findings and, over time, gauge whether your testers are increasing their effort to find issues (stated differently, do they find fewer bugs in the same amount of time?). If these metrics don't move in the right direction, then one might conclude that your security practices are also not improving (providing material for decision making).

Measuring effort, or attacker cost, is not new to security but it's mostly done indirectly through the sale of exploits (e.g. iDefence, ZDI). Even here, effort is not directly related to the purchase price, which is also influenced by other factors such as the number of deployed targets etc. In any case, for custom applications that testers are mostly presented with, such public sources should be of little help (if your testers are submitting findings to ZDI, you have bigger problems). Every now and then, an exploit dev team will mention how long it took them to write an exploit for some weird Windows bug; these are always interesting data points, but are not specific enough for customers and the sample size is low.

Ideally, any measure of an attacker's cost can take into account both time and their exclusivity (or experience), however in practice this will be tough to gather from your testers. One could base it on their hourly rate, if your testing company differentiates between resources. In cases where they don't, or you're seeking to keep the metric simple, then another estimate for effort is the number of days spent on testing.

Returning to our sample companies, if the 5 vulnerabilities exposed in the Visigoth's each required, on average, a single day to find, while the Ostrogoth's 20 bugs average 5 days each, then the effort required by an attacker is minimised by choosing to target the Visigoths. In other words, one might argue that the Visigoths are more at risk than the Ostrogoths.

Metricload, take 1

In our first stab at incorporating effort, we selected an estimator of findings-per-day (or finding rate) to be the base metric against which the impact, classification, time-series and vertical attributes would be measured. From this, it's apparent that, subject to some minimum, the number of assessments performed is less important than the number of days worked. I don't yet have a way to answer what the minimum number of assessments should be, but it's clear that comparing two organisations where one has engaged with us 17 times and the other once, won't yield reliable results.

With this base metric, it's then possible to capture historical assessment data and provide both internal-looking metrics for an organisation as well as comparative metrics, if the testing company is also employed by your competitors. Internal metrics are the usual kinds (impact, classification, time-series), but the comparison option is very interesting. We're in the fortunate position of working with many top companies locally, and are able to compare competitors using this metric as a base. The actual ranking formulae is largely unimportant here. Naturally, data must be anonymised so as to protect names; one could provide the customer with their rank only. In this way, the customer has an independent notion of how their security activities rate against their peers without embarrassing the peers.

Inverting the findings-per-day metric provide the average number of days to find a particular class of vulnerability, or impact level. That is, if a client averages 0.7 High or Critical findings per testing day, then on average it takes us 1.4 days of testing to find an issue of great concern, which is an easy way of expressing the base metric.

What, me worry?

Without doubt, the findings-per-day estimator has drawbacks. For one, it doesn't take into consideration the tester's skill level (but this is also true of all appsec metrics published). This could be extended to include things like hourly rates, which indirectly measure skill. Also, the metric does not take into account functionality exposed by the site; if an organisation has only brochure-ware sites then it's unfair to compare them against transactional sites; this is mitigated at the time of analysis by comparing against peers rather than the entire sample group and also, to a degree, in the scoping of the project as a brochure-ware site would receive minimum testing time if scoped correctly.

As mentioned above, a minimum number of assessments would be needed before the metric is reliable; this is a hint at the deeper problems that randomly selected project days are not independent. An analyst stuck on a 4 week project is focused on a very small part of the broader organisation's application landscape. We counter this bias by including as many projects of the same type as possible.

Thought.rand()

If you can tease it out of them, finding rates could be an interesting method of comparing competing testing companies; ask "when testing companies of our size and vertical, what is your finding rate?", though there'd be little way to verify any claims. Can you foresee a day when testing companies advertise using their finding rate as the primary message? Perhaps...

This metric would also be very useful to include in each subsequent report for the customer, with every report containing an evaluation against their longterm vulnerability averages.

Field testing

Using the above findings-per-day metric as a base, we performed an historical analysis for a client on work performed over a number of years, with a focus on answering the following questions for them:

1. On average, how long does it take to find issues at each Impact level (Critical down to Informational)?
2. What are the trends for the various vulnerability classes? Does it take more or less time to find them year-on-year?
3. What are the Top 10 issues they're currently facing?
4. Where do they stand in relation to anonymised competitor data?

In preparation for the exercise, we had to capture a decent number of past reports, which was most time-consuming. What this highlighted for us was how paper-based reports and reporting is a serious hinderance to extracting useful data, and has provided impetus internally for us to look into alternatives. The derived statistics were presented to the client in a workshop, with representatives from a number of the customer's teams present. We had little insight into the background to many of the projects, and it was very interesting to hear the analysis and opinions that emerged as they digested the information. For example, one set of applications exhibited particularly poor metrics from a security standpoint. Someone highlighted the fact that these were outsourced applications, which raised a discussion within the client about the pros and cons on using third party developers. It also suggests that many further attributes can be attached to the data that is captured: internal or third party, development lifecycle model (is agile producing better code for you than other models?), team size, platforms, languages, frameworks etc.

As mentioned above, a key test for metrics is where they support decision making, and the feedback from the client was positive in this regard.

And now?

In summary, current security metrics as they relate to talking about an organisation's application security suffers from a resolution problem; they're not clear enough. Attacker effort is not modeled when discussing vulnerabilities, even though it's a significant factor when trying to get a handle on the ever slippery notion of risk. One approximation for attacker effort is to create a base-metric of the number of findings-per-day for a broad set of applications belonging to an organisation, and use those to evaluate which kinds of vulnerabilities are typically present while at the same time clarifying how much effort an attacker requires in order to exploit it.

This idea is still being fleshed out. If you're aware of previous work in this regard or have suggestions on how to improve it (even abandon it) please get in contact.

Oh, and if you've read this far and are looking for training, we're at BH in August.

Ron Auger sent an email to the [WASC Mail list] on some fine work presented recently by Microsoft Research. The paper (and accompanying PPT), titled [Pretty-Bad-Proxy: An Overlooked Adversary in Browsers' HTTPS Deployments] is pretty cool and shows several techniques for a malicious inline proxy to sniff SSL sessions passing through the proxy. Its genuinely a bunch of cool findings and has been handled neatly (with the exception of some shocking clipart!).

The attack logic is fairly simple. User tries to browse to https://mybank.com. The browser sends a connect message to the proxy. The proxy returns an HTTP 502 proxy error message. The magic comes in here. The browser interprets the returned 502 message within the security context of https://mybank.com.

So the attack works as follows:

1. User tries to browse to https://mybank.com.
2. Browser sends connect message to evil-proxy.
3. Evil-proxy sends back a 502 message, with evil-Javascript and opens an iframe to https://mybank.com.
4. Evil-proxy lets the request go to https://mybank.com and the page loads in the users browser.
5. Evil-Javascript is now running in the same context as the banking session, so it has full access to page..

Elite!

With a little more arm bending the paper even goes on to remove the neccessity of full control of an evil proxy, relying on an attacker on the local network sniffing traffic and then racing the valid proxy server..

The findings have been disclosed to the browser vendors and have already been remediated, which means we can collectively breath a sigh of relief, but clearly, it has not been a good year for SSL (and SSL implementations).

aka.. Someone put the hurtski on Kaspersky..

The Twitters (via XSSniper and others) and the Interwebs were ablaze with news on a SQL Injection vulnerability that was exploited on AV vendor Kaspersky's site. Detail of the attack can be found here.

It's interesting that SQL Injection (though as old as the proverbial hills) is still such a major issue. In fact, I have it on good authority that the bulk of PCI-related compromises are still as a result of SQL Injection...

In our own work, we see this all over the show.

Also interesting is the fact that the DB in use by Kaspersky is MySQL - so much for the "I don't use MSSQL, I have x database with magical pixie dust SQL Injection protection - what me worry?" argument...

Once again, security one-oh-one...if you aren't *effectively* validating user input, you're going to get bitten some time...

/nick

ED* From the shameless self promotion department:

haroon and Marco have just finished their chapters in an upcoming book dedicated to SQL Injection. We will post more details here when its available. (the book aims to give SQL Injection thorough coverage from OR 1=1 to some of the insanity demo'd at BlackHat last year..)

By now everyone knows that John McCain's running mate Sarah Palin had her yahoo email account hacked. I guess a presidential candidate using yahoo for govt. related email was about as shocking as Sarah Palins nomination as possible future president ((unless of course you have ever heard of other govt. officials using yahoo/gmail/hotmail for serious business)(inside joke for south africans!)).

People have been talking about secure password resets for a long time [1] and this was pretty shocking all around..

But even more shocking for me (as a totally removed observer), was the Errata Security post (authors of hamster, which we commented on [here]) ending their post with an endorsement of the McCain/Palin ticket.. i thought all (american) hax0rs leaned towards "the change"

So - here's the scenario.

Lohan is busy testing an application which uses remote web-services on a server called (example) www.target.com, but the program bypasses all proxy servers etc, making it impossible to trap and mangle requests.

So, we do the following:

1 - We make a note of the IP address of www.target.com (in this case, we'll assume it is 196.310.150.126 )

2 - Add a host entry in hosts, mapping www.target.com to 127.0.0.1

3 - Fire up a quick C# app written by yours truly which listens on 127.0.0.1:80

4 - Fire up a proxy server

5 - Configure the C# app to use proxy server 127.0.0.1:port of proxy
Now, the C# app does the following:

1 - Intercepts the HTTP request addressed to www.target.com

2 - Mangles the HTTP request to convert it into a proxied request (ie: Request "GET / HTTP/1.0" now becomes "GET http://196.31.150.216/ HTTP/1.0")

3 - Writes the request to the proxy server

4 - Writes the response back to the application

So, we're now able to intercept, fuzz, mangle etc all the requests and responses between the application and the web service. Not really rocket science, but rather handy...

The screenshot shows something similar, but using a web browser in place of the application here. I am using paros in this example because I am still doing large chunks of work on Suru...

/ian