BIML Releases First Risk Framework for Securing Machine Learning Systems

BERRYVILLE, Va., Feb. 13, 2020 – The Berryville Institute of Machine Learning (BIML), a research think tank dedicated to safe, secure and ethical development of AI technologies, today released the first-ever risk framework to guide development of secure ML. The “Architectural Risk Analysis of Machine Learning Systems: Toward More Secure Machine Learning” is designed for use by developers, engineers, designers and others who are creating applications and services that use ML technologies.

Early work on ML security focuses on specific failures, including systems that learn to be sexist, racist and xenophobic like Microsoft’s Tay, or systems that can be manipulated into seeing a STOP sign as a speed limit sign using a few pieces of tape. The BIML ML Security Risk Framework details the top 10 security risks in ML systems today. A total of 78 risks have been identified by BIML using a generic ML system as an organizing concept. The BIML ML Security Risk Framework can be practically applied in the early design and development phases of any ML project.

“The tech industry is racing ahead with AI and ML with little to no consideration for the security risks that automated machine learning poses,” says Dr. Gary McGraw, co-founder of BIML. “We saw with the development of the internet the consequences of security as an afterthought. But with AI we have the chance now to do it right.” 

For more information about An Architectural Risk Analysis of Machine Learning Systems: Toward More Secure Machine Learning, visit https://berryvilleiml.com/results/.  

A link to the PR on the wire: https://onlineprnews.com//news/1143530-1581535720-biml-releases-first-risk-framework-for-securing-machine-learning-systems.html

First MLsec talk on the BIML ARA Delivered Ultra Locally

The first talk on BIML’s new Architectural Risk Analysis of Machine Learning Systems was delivered this Wednesday at Lord Fairfax Community College. The talk was well attended and included a remote audience attending virtually. The Winchester Star published a short article about the talk.

Berryville Institute of Machine Learning (BIML) is located in Clarke County, Virginia, an area served by Lord Fairfax Community College.

On recent Microsoft and NIST ML security documents

Recently there have been several documents published as guides to security in machine learning. In October 2019, NIST published a draft called “A Taxonomy and Terminology of Adversarial Machine Learning”. Then in November, Microsoft published several interrelated webpages laying out a threat model for AI/ML systems and tying it to MS’s existing Software Development Lifecycle. We took a look at these documents to find out what they are trying to do, what they do well, and what they lack.

The NIST document is a tool for navigating MLsec literature, somewhat in the vein of an academic survey paper but accessible to those outside the field. The focus is explicitly “adversarial ML”, i.e. the failures a motivated attacker can induce in an ML system through input. They present a taxonomy of concepts in the literature rather than covering specific attacks or risks. Also included is a technical terminology with definitions, synonyms and references to the originating papers. The taxonomy at first appeared conceptually bizarre to us, but we came to see it as a powerful tool for a particular task: working backward from an unfamiliar technical term to its root concept and related ideas. In this way the NIST document may be very helpful to non-ML experts concerned with security attempting to wrangle the ML security literature.

The Microsoft effort is a three-headed beast:

  • “Failure Modes in Machine Learning”, a brief taxonomy of 16 intentional and unintentional failures. It supposedly meets “the need to equip software developers, security incident responders, lawyers, and policy makers with a common vernacular to talk about this problem”. To this end the authors avoid technical language where possible. Each threat is classified using the somewhat dated and quaint Confidentiality/Integrity/Availability security model. This is easy enough to understand, though we find the distinction between Integrity and Availability attacks unclear for most ML scenarios. The unintentional failures are oddly fixated on Reinforcement Learning, and several seem to boil down to the same thing. For example #16 “Common Corruption” appears to be a subcategory of #14 “Distributional Shifts.”
  • “AI/ML Pivots to the Security Development Lifecycle Bug Bar”, similar to the above but aimed at a different audience, “as a reference for the triage of AI/ML-related security issues”. This section presents materials for use while applying some of the standard Microsoft SDL processes.  Of interest is the fact that threat modeling is emphasized in its own section.  We approve of that move.
  • “Threat Modeling AI/ML Systems and Dependencies”  is the most detailed component, containing the meat of the Microsoft MLsec effort. Here you can find security review checklists and a survey paper-style elaboration of each major risk with an emphasis on mitigations. The same eleven categories of “intentional failures” are used as in the other documents. However, (at the time of writing) the unintentional failures are left out. We found the highlighting of risk #6 “Neural Net Reprogramming” particularly interesting, as it had been unknown to us before. This work shows how adversarial examples can be used to do a kind of arbitrage where a service provided at cost (say, automatically tagging photos in a cloud storage account) can be repurposed to a similar task like breaking CAPTCHAs.

The Microsoft documents function as practical tools for securing software, including checklists for a security review and lists of potential mitigations. However, we find their categorizations confusing or redundant in places. Laudably, they move beyond adversarial ML to the concept of “unintentional failures”. But unfortunately, these failure modes remain mostly unelaborated in the more detailed documents.

Adversarial/intentional failures are important, but we shouldn’t neglect the unintentional ones. Faulty evaluation, unexpected distributional shifts, mis-specified models, and unintentional reproduction of data biases can all threaten the efficacy, safety and fairness of every ML system. Both the Microsoft and NIST documents are tools for an organization seeking to secure itself against external threats. But equally important to secure against is the misuse of AI/ML.

Use Your Community Resources [Principle 10]

Community resources can be a double-edged sword; on the one hand, systems that have faced public scrutiny can benefit from the collective effort to break them. But nefarious individuals aren’t interested in publicizing the flaws they identify in open systems, and even large communities of developers have trouble resolving all of the flaws in such systems. Relying on publicly available information can expose your own system to risks, particularly if an attacker is able to identify similarities between your system and public ones.  

Transfer learning is a particularly relevant issue to ML systems. While transfer learning has demonstrated success in applying the learned knowledge of an ML system to other problems, knowledge of the base model can sometimes be used to attack the student [wang18]. In a more general sense, the use of publicly available models and hyperparameters could expose ML systems to particular attacks. How do engineers know that a model they use wasn’t deliberately made public for this very purpose? 

Public datasets used to train ML algorithms are another important concern. Engineers need to take care to validate the authenticity and quality of any public datasets they use, especially when that data could have been manipulated by unknown parties.  At the core of these concerns is the matter of trust; if the community can be trusted to effectively promote the security of their tools, models, and data, then community resources can be hesitantly used. Otherwise, it would be better to avoid exposing systems to unnecessary risk. After all, security problems in widely-used open-source projects have been known to persist for years, and in some cases decades, before the community finally took notice.

Read the rest of the principles here.

Be Reluctant to Trust [Principle 9]

ML systems rely on a number of possibly untrusted, external sources for both their data and their computation. Let’s take on data first. Mechanisms used to collect and process data for training and evaluation make an obvious target. Of course, ML engineers need to get their data somehow, and this necessarily invokes the question of trust. How does an ML system know it can trust the data it’s being fed? And, more generally, what can the system do to evaluate the collector’s trustworthiness? Blindly trusting sources of information would expose the system to security risks and must be avoided.

Next, let’s turn to external sources of computation. External tools such as TensorFlow, Kubeflow, and pip can be evaluated based on the security expertise of their engineers, time-proven resilience to attacks, and their own reliance on further external tools, among other metrics. Nonetheless, it would be a mistake to assume that any external tool is infallible. Systems need to extend as little trust as possible, in the spirit of compartmentalization, to minimize the capabilities of threats operating through external tools.

It can help to think of the various components of an ML system as extending trust to one another; dataset assembly could trust the data collectors’ organization of the data, or it could build safeguards to ensure normalization. The inference algorithm could trust the model’s obfuscation of training data, or it could avoid responding to queries that are designed to extract sensitive information. Sometimes it’s more practical to trust certain properties of the data, or various components, but in the interests of secure design only a minimum amount of trust should be afforded. Building more security into each component makes attacks much more difficult to successfully orchestrate.

Read the rest of the principles here.

Remember that Hiding Secrets is Hard [Principle 8]

Security is often about keeping secrets. Users don’t want their personal data leaked. Keys must be kept secret to avoid eavesdropping and tampering. Top-secret algorithms need to be protected from competitors. These kinds of requirements are almost always high on the list, but turn out to be far more difficult to meet than the average user may suspect.

ML system engineers may want to keep the intricacies of their system secret, including the algorithm and model used, hyperparameter and configuration values, and other details concerning how the system trains and performs. Maintaining a level of secrecy is a sound strategy for improving the security of the system, but it should not be the only mechanism.

Past research in transfer learning has demonstrated the ability for new ML systems to be trained from existing ones. If transfer learning is known to have been applied, it may facilitate extraction of the proprietary layers trained “on top” of the base model. Even when the base model is not known, distillation attacks allow an attacker to copy the possibly proprietary behavior of a model using only the ability to query the ML system externally.  As a result, maintaining the secrecy of the system’s design requires more than simply not making the system public knowledge.

A chief concern for ML systems is protecting the confidentiality of training data. Some may attempt to “anonymize” the data used and consider that sufficient. As the government of Australia discovered in 2017, great care must be taken in determining that the data cannot be deanonymized1. Neural networks similarly provide a layer of anonymization by transforming confidential information into weights, but even those weights can be vulnerable to advanced information extraction techniques. It’s up to system engineers to identify the risks inherent in their system and design protection mechanisms that minimize security exposure. 

Keeping secrets is hard, and it is almost always a source of security risk.

Read the rest of the principles here.


1. Culnane, Chris, Benjamin Rubinstein, Vanessa Teague. “Understanding the Maths is Crucial for Protecting Privacy.” Technical Report from Department of Computing and Information Systems, University of Melbourne. (Published Sept 29, 2016; Accessed Oct 28, 2019.)

Promote Privacy [Principle 7]

Privacy is tricky even when ML is not involved. ML makes things ever trickier by in some sense re-representing sensitive and/or confidential data inside of the machine.  This makes the original data “invisible” (at least to some users), but remember that the data are still in some sense “in there somewhere.”  So, for example, if you train a classifier on sensitive medical data and you don’t consider what will happen when an attacker tries to get those data back out through a set of sophisticated queries, you may not be doing your job.

When it comes to sensitive data, one promising approach in privacy-preserving ML is differential privacy. The idea behind differential privacy is to set up privacy restrictions that, for example, guarantee that an individual patient’s private medical data never has too much influence on a dataset or on a trained ML system.  The idea is to in some sense “hide in plain sight” with a goal of ensuring that anything that can be learned about an individual from the released information, can also be learned without that individual’s data being included.  An algorithm is differentially private if an observer examining the output is not able to determine whether a specific individual’s information was used in the computation.  Differential privacy is achieved through the use of random noise that is generated according to a chosen distribution and is used to perturb a true answer.  Somewhat counterintuitively, because of its use of noise, differential privacy can also be used to combat overfitting in some ML situations.  Differential privacy is a reasonably promising line of research that can in some cases provide for privacy protection.

      Privacy also applies to the behavior of a trained-up ML system in operation.  We’ve discussed the tradeoffs associated with providing (or not providing) confidence scores.  Sometimes that’s a great idea, and sometimes it’s not. Figuring out the impact on system security that providing confidence scores will have is another decision that should be explicitly considered and documented.

      In short, you will do well to spend some cycles thinking about privacy in your ML system while you’re thinking about security.

Read the rest of the principles here.

Keep it Simple [Principle 6]

 Keep It Simple, Stupid (often spelled out KISS) is good advice when it comes to security. Complex software (including most ML software) is at much greater risk of being inadequately implemented or poorly designed than simple software is, causing serious security challenges. Keeping software simple is necessary to avoid problems related to efficiency, maintainability, and of course, security.  But software is by its very nature complex.

Machine Learning seems to defy KISS by its very nature. ML models involve complex mathematics that is often poorly understood by implementers. ML frequently relies on huge amounts of data that can’t possibly be fully understood and vetted by system engineers. As a result, many ML systems are vulnerable to numerous attacks arising from complexity. It is important for implementers of ML systems to recognize the drawbacks of using complex ML algorithms and to build security controls around them. Adding controls to an already complex system may seem to run counter to our simplicity goal, but sometimes security demands more. Striking a balance between achieving defense-in-depth and simplicity, for example, is a tricky task.

KISS should help inform ML algorithm selection. What makes an adequate algorithm varies according to the goals and requirements of the system, yet there are often multiple choices. When such a choice needs to be made, it is important to consider not only the accuracy claims made by designers of the algorithm, but also how well the algorithm itself is understood by engineers and the broader research community. If the engineers developing the ML system don’t deeply understand the underlying algorithm they are using, they are more likely to miss security problems that arise during operations. This doesn’t necessarily mean that the latest and greatest algorithms can’t be used, but rather that engineers need to be cognizant of the amount of time and effort it takes to understand and then build upon complex systems.

Read the rest of the principles here.

Compartmentalize [Principle 5]


The figure above shows how we choose to represent a generic ML system. Note that in our generic model, both processes and collections are treated as components. Processes are represented by ovals, whereas artifacts and collections of artifacts are represented as rectangles.

The risk analysis of the generic ML system above uses a set of nine “components” to help categorize and explain risks found in various logical pieces.  Components can be either processes or collections. Just as understanding a system is easier when a system is divided up into pieces, controlling security risk is easier when the pieces themselves are each secured separately.  Another way of thinking about this is to compare old fashioned “monolithic” software design to “micro-services” design.  In general, both understanding and securing a monolith is much harder than securing a set of services (of course things get tricky when services interact in time, but we’ll ignore that for now).  In the end we want to eradicate the monolith and use compartmentalization as our friend.

      Lets imagine one security principle and see how compartmentalization can help us think it through.  Part of the challenge of applying the principle of least privilege in practice (described above) has to do with component size and scope.  When building blocks are logically separated and structured, applying the principle of least privilege to each component is much more straightforward than it would be otherwise.  Smaller components should by and large require less privilege than complete systems.  Does this component involve pre-processed training data that will directly impact system learning?  Hmm, better secure those data!

     The basic idea behind compartmentalization is to minimize the amount of damage that can be done to a system by breaking up the system into a number of units and isolating processes or data that carry security privilege. This same principle explains why submarines are built with many different chambers, each separately sealed. If a breach in the hull causes one chamber to fill with water, the other chambers are not affected. The rest of the ship can keep its integrity, and people can survive by making their way to parts of the submarine that are not flooded.

      The challenge with security and compartmentalization comes when it is time to consider the system as a whole.  As we’ve seen in our generic ML system here, data flows between components, and sometimes those data are security sensitive.  When implementing an ML system, considering component risks is a good start, but don’t forget to think through the risks of the system as a whole.  Harkening back to the principle of least privilege, don’t forget to apply the same sort of thinking to the system as a whole after you have completed working on the components.

Read the rest of the principles here.

Follow the Principle of Least Privilege [Principle 4]

The principle of least privilege states that only the minimum access necessary to perform an operation should be granted, and that access should be granted only for the minimum amount of time necessary.[i]

When you give out access to parts of a system, there is always some risk that the privileges associated with that access will be abused. For example, let’s say you are to go on vacation and you give a friend the key to your home, just to feed pets, collect mail, and so forth. Although you may trust the friend, there is always the possibility that there will be a party in your house without your consent, or that something else will happen that you don’t like. Regardless of whether you trust your friend, there’s really no need to put yourself at risk by giving more access than necessary. For example, if you don’t have pets, but only need a friend to pick up the mail on occasion, you should relinquish only the mailbox key. Although your friend may find a good way to abuse that privilege, at least you don’t have to worry about the possibility of additional abuse. If you give out the house key unnecessarily, all that changes.

Similarly, if you do get a house-sitter while you’re on vacation, you aren’t likely to let that person keep your keys when you’re not on vacation. If you do, you’re setting yourself up for additional risk. Whenever a key to your house is out of your control, there’s a risk of that key getting duplicated. If there’s a key outside your control, and you’re not home, then there’s the risk that the key is being used to enter your house. Any length of time that someone has your key and is not being supervised by you constitutes a window of time in which you are vulnerable to an attack. You want to keep such windows of vulnerability as short as possible to minimize your risks.

In an ML system, we most likely want to control access around lifecycle phases. In the training phase, the system may have access to lots of possibly sensitive training data.  Assuming an offline model (where training is not continuous), after the training phase is complete, the system should no longer require access to those data. (As we discussed when we were talking defense in depth, system engineers need to understand that in some sense all of the confidential data are now represented in the trained-up ML system and may be subject to ML-specific attacks.)

Thinking about access control in an ML is useful and can be applied through the lens of the principle of least privilege, particularly between lifecycle phases and system components. Users of an ML system are not likely to need access to training data and test data, so don’t give it to them. In fact, users may only require black box API access to a running system.  If that’s the case, then provide only what is necessary in order to preserve security.

Less is more when it comes to the principle of least privilege. Limit data exposure to those components that require it and then for as short a time period as possible.

Read the rest of the principles here.


[i]Saltzer, Jerome H., and Michael D. Schroeder. “The protection of information in computer systems.” Proceedings of the IEEE63, no. 9 (1975): 1278-1308.