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Security Vulnerabilities in Cell Phone Systems

Good essay on the inherent vulnerabilities in the cell phone standards and the market barriers to fixing them.

So far, industry and policymakers have largely dragged their feet when it comes to blocking cell-site simulators and SS7 attacks. Senator Ron Wyden, one of the few lawmakers vocal about this issue, sent a letter in August encouraging the Department of Justice to “be forthright with federal courts about the disruptive nature of cell-site simulators.” No response has ever been published.

The lack of action could be because it is a big task — there are hundreds of companies and international bodies involved in the cellular network. The other reason could be that intelligence and law enforcement agencies have a vested interest in exploiting these same vulnerabilities. But law enforcement has other effective tools that are unavailable to criminals and spies. For example, the police can work directly with phone companies, serving warrants and Title III wiretap orders. In the end, eliminating these vulnerabilities is just as valuable for law enforcement as it is for everyone else.

As it stands, there is no government agency that has the power, funding and mission to fix the problems. Large companies such as AT&T, Verizon, Google and Apple have not been public about their efforts, if any exist.

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Machine Learning to Detect Software Vulnerabilities

No one doubts that artificial intelligence (AI) and machine learning (ML) will transform cybersecurity. We just don’t know how, or when. While the literature generally focuses on the different uses of AI by attackers and defenders ­ and the resultant arms race between the two ­ I want to talk about software vulnerabilities.

All software contains bugs. The reason is basically economic: The market doesn’t want to pay for quality software. With a few exceptions, such as the space shuttle, the market prioritizes fast and cheap over good. The result is that any large modern software package contains hundreds or thousands of bugs.

Some percentage of bugs are also vulnerabilities, and a percentage of those are exploitable vulnerabilities, meaning an attacker who knows about them can attack the underlying system in some way. And some percentage of those are discovered and used. This is why your computer and smartphone software is constantly being patched; software vendors are fixing bugs that are also vulnerabilities that have been discovered and are being used.

Everything would be better if software vendors found and fixed all bugs during the design and development process, but, as I said, the market doesn’t reward that kind of delay and expense. AI, and machine learning in particular, has the potential to forever change this trade-off.

The problem of finding software vulnerabilities seems well-suited for ML systems. Going through code line by line is just the sort of tedious problem that computers excel at, if we can only teach them what a vulnerability looks like. There are challenges with that, of course, but there is already a healthy amount of academic literature on the topic — and research is continuing. There’s every reason to expect ML systems to get better at this as time goes on, and some reason to expect them to eventually become very good at it.

Finding vulnerabilities can benefit both attackers and defenders, but it’s not a fair fight. When an attacker’s ML system finds a vulnerability in software, the attacker can use it to compromise systems. When a defender’s ML system finds the same vulnerability, he or she can try to patch the system or program network defenses to watch for and block code that tries to exploit it.

But when the same system is in the hands of a software developer who uses it to find the vulnerability before the software is ever released, the developer fixes it so it can never be used in the first place. The ML system will probably be part of his or her software design tools and will automatically find and fix vulnerabilities while the code is still in development.

Fast-forward a decade or so into the future. We might say to each other, “Remember those years when software vulnerabilities were a thing, before ML vulnerability finders were built into every compiler and fixed them before the software was ever released? Wow, those were crazy years.” Not only is this future possible, but I would bet on it.

Getting from here to there will be a dangerous ride, though. Those vulnerability finders will first be unleashed on existing software, giving attackers hundreds if not thousands of vulnerabilities to exploit in real-world attacks. Sure, defenders can use the same systems, but many of today’s Internet of Things systems have no engineering teams to write patches and no ability to download and install patches. The result will be hundreds of vulnerabilities that attackers can find and use.

But if we look far enough into the horizon, we can see a future where software vulnerabilities are a thing of the past. Then we’ll just have to worry about whatever new and more advanced attack techniques those AI systems come up with.

This essay previously appeared on

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Measuring the Rationality of Security Decisions

Interesting research: “Dancing Pigs or Externalities? Measuring the Rationality of
Security Decisions

Abstract: Accurately modeling human decision-making in security is critical to thinking about when, why, and how to recommend that users adopt certain secure behaviors. In this work, we conduct behavioral economics experiments to model the rationality of end-user security decision-making in a realistic online experimental system simulating a bank account. We ask participants to make a financially impactful security choice, in the face of transparent risks of account compromise and benefits offered by an optional security behavior (two-factor authentication). We measure the cost and utility of adopting the security behavior via measurements of time spent executing the behavior and estimates of the participant’s wage. We find that more than 50% of our participants made rational (e.g., utility optimal) decisions, and we find that participants are more likely to behave rationally in the face of higher risk. Additionally, we find that users’ decisions can be modeled well as a function of past behavior (anchoring effects), knowledge of costs, and to a lesser extent, users’ awareness of risks and context (R2=0.61). We also find evidence of endowment effects, as seen in other areas of economic and psychological decision-science literature, in our digital-security setting. Finally, using our data, we show theoretically that a “one-size-fits-all” emphasis on security can lead to market losses, but that adoption by a subset of users with higher risks or lower costs can lead to market gains

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GCHQ on Quantum Key Distribution

The UK’s GCHQ delivers a brutally blunt assessment of quantum key distribution:

QKD protocols address only the problem of agreeing keys for encrypting data. Ubiquitous on-demand modern services (such as verifying identities and data integrity, establishing network sessions, providing access control, and automatic software updates) rely more on authentication and integrity mechanisms — such as digital signatures — than on encryption.

QKD technology cannot replace the flexible authentication mechanisms provided by contemporary public key signatures. QKD also seems unsuitable for some of the grand future challenges such as securing the Internet of Things (IoT), big data, social media, or cloud applications.

I agree with them. It’s a clever idea, but basically useless in practice. I don’t even think it’s anything more than a niche solution in a world where quantum computers have broken our traditional public-key algorithms.

Read the whole thing. It’s short.

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On Financial Fraud

There are some good lessons in this article on financial fraud:

That’s how we got it so wrong. We were looking for incidental breaches of technical regulations, not systematic crime. And the thing is, that’s normal. The nature of fraud is that it works outside your field of vision, subverting the normal checks and balances so that the world changes while the picture stays the same. People in financial markets have been missing the wood for the trees for as long as there have been markets.


Trust — particularly between complete strangers, with no interactions beside relatively anonymous market transactions — is the basis of the modern industrial economy. And the story of the development of the modern economy is in large part the story of the invention and improvement of technologies and institutions for managing that trust.

And as industrial society develops, it becomes easier to be a victim. In The Wealth of Nations, Adam Smith described how prosperity derived from the division of labour — the 18 distinct operations that went into the manufacture of a pin, for example. While this was going on, the modern world also saw a growing division of trust. The more a society benefits from the division of labour in checking up on things, the further you can go into a con game before you realise that you’re in one.


Libor teaches us a valuable lesson about commercial fraud — that unlike other crimes, it has a problem of denial as well as one of detection. There are very few other criminal acts where the victim not only consents to the criminal act, but voluntarily transfers the money or valuable goods to the criminal. And the hierarchies, status distinctions and networks that make up a modern economy also create powerful psychological barriers against seeing fraud when it is happening. White-collar crime is partly defined by the kind of person who commits it: a person of high status in the community, the kind of person who is always given the benefit of the doubt.


Fraudsters don’t play on moral weaknesses, greed or fear; they play on weaknesses in the system of checks and balances — the audit processes that are meant to supplement an overall environment of trust. One point that comes up again and again when looking at famous and large-scale frauds is that, in many cases, everything could have been brought to a halt at a very early stage if anyone had taken care to confirm all the facts. But nobody does confirm all the facts. There are just too bloody many of them. Even after the financial rubble has settled and the arrests been made, this is a huge problem.

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Department of Commerce Report on the Botnet Threat

Last month, the US Department of Commerce released a report on the threat of botnets and what to do about it. I note that it explicitly said that the IoT makes the threat worse, and that the solutions are largely economic.

The Departments determined that the opportunities and challenges in working toward dramatically reducing threats from automated, distributed attacks can be summarized in six principal themes.

  1. Automated, distributed attacks are a global problem. The majority of the compromised devices in recent noteworthy botnets have been geographically located outside the United States. To increase the resilience of the Internet and communications ecosystem against these threats, many of which originate outside the United States, we must continue to work closely with international partners.

  2. Effective tools exist, but are not widely used. While there remains room for improvement, the tools, processes, and practices required to significantly enhance the resilience of the Internet and communications ecosystem are widely available, and are routinely applied in selected market sectors. However, they are not part of common practices for product development and deployment in many other sectors for a variety of reasons, including (but not limited to) lack of awareness, cost avoidance, insufficient technical expertise, and lack of market incentives

  3. Products should be secured during all stages of the lifecycle. Devices that are vulnerable at time of deployment, lack facilities to patch vulnerabilities after discovery, or remain in service after vendor support ends make assembling automated, distributed threats far too easy.

  4. Awareness and education are needed. Home users and some enterprise customers are often unaware of the role their devices could play in a botnet attack and may not fully understand the merits of available technical controls. Product developers, manufacturers, and infrastructure operators often lack the knowledge and skills necessary to deploy tools, processes, and practices that would make the ecosystem more resilient.

  5. Market incentives should be more effectively aligned. Market incentives do not currently appear to align with the goal of “dramatically reducing threats perpetrated by automated and distributed attacks.” Product developers, manufacturers, and vendors are motivated to minimize cost and time to market, rather than to build in security or offer efficient security updates. Market incentives must be realigned to promote a better balance between security and convenience when developing products.

  6. Automated, distributed attacks are an ecosystem-wide challenge. No single stakeholder community can address the problem in isolation.


The Departments identified five complementary and mutually supportive goals that, if realized, would dramatically reduce the threat of automated, distributed attacks and improve the resilience and redundancy of the ecosystem. A list of suggested actions for key stakeholders reinforces each goal. The goals are:

  • Goal 1: Identify a clear pathway toward an adaptable, sustainable, and secure technology marketplace.
  • Goal 2: Promote innovation in the infrastructure for dynamic adaptation to evolving threats.
  • Goal 3: Promote innovation at the edge of the network to prevent, detect, and mitigate automated, distributed attacks.
  • Goal 4: Promote and support coalitions between the security, infrastructure, and operational technology communities domestically and around the world
  • Goal 5: Increase awareness and education across the ecosystem.

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Regulating Bitcoin

Ross Anderson has a new paper on cryptocurrency exchanges. From his blog:

Bitcoin Redux explains what’s going wrong in the world of cryptocurrencies. The bitcoin exchanges are developing into a shadow banking system, which do not give their customers actual bitcoin but rather display a “balance” and allow them to transact with others. However if Alice sends Bob a bitcoin, and they’re both customers of the same exchange, it just adjusts their balances rather than doing anything on the blockchain. This is an e-money service, according to European law, but is the law enforced? Not where it matters. We’ve been looking at the details.

The paper.

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Estimating the Cost of Internet Insecurity

It’s really hard to estimate the cost of an insecure Internet. Studies are all over the map. A methodical study by RAND is the best work I’ve seen at trying to put a number on this. The results are, well, all over the map:

Estimating the Global Cost of Cyber Risk: Methodology and Examples“:

Abstract: There is marked variability from study to study in the estimated direct and systemic costs of cyber incidents, which is further complicated by the considerable variation in cyber risk in different countries and industry sectors. This report shares a transparent and adaptable methodology for estimating present and future global costs of cyber risk that acknowledges the considerable uncertainty in the frequencies and costs of cyber incidents. Specifically, this methodology (1) identifies the value at risk by country and industry sector; (2) computes direct costs by considering multiple financial exposures for each industry sector and the fraction of each exposure that is potentially at risk to cyber incidents; and (3) computes the systemic costs of cyber risk between industry sectors using Organisation for Economic Co-operation and Development input, output, and value-added data across sectors in more than 60 countries. The report has a companion Excel-based modeling and simulation platform that allows users to alter assumptions and investigate a wide variety of research questions. The authors used a literature review and data to create multiple sample sets of parameters. They then ran a set of case studies to show the model’s functionality and to compare the results against those in the existing literature. The resulting values are highly sensitive to input parameters; for instance, the global cost of cyber crime has direct gross domestic product (GDP) costs of $275 billion to $6.6 trillion and total GDP costs (direct plus systemic) of $799 billion to $22.5 trillion (1.1 to 32.4 percent of GDP).

Here’s Rand’s risk calculator, if you want to play with the parameters yourself.

Note: I was an advisor to the project.

Separately, Symantec has published a new cybercrime report with their own statistics.

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