Entries Tagged "reports"

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Bug Bounty Programs Are Being Used to Buy Silence

Investigative report on how commercial bug-bounty programs like HackerOne, Bugcrowd, and SynAck are being used to silence researchers:

Used properly, bug bounty platforms connect security researchers with organizations wanting extra scrutiny. In exchange for reporting a security flaw, the researcher receives payment (a bounty) as a thank you for doing the right thing. However, CSO’s investigation shows that the bug bounty platforms have turned bug reporting and disclosure on its head, what multiple expert sources, including HackerOne’s former chief policy officer, Katie Moussouris, call a “perversion.”

[…]

Silence is the commodity the market appears to be demanding, and the bug bounty platforms have pivoted to sell what willing buyers want to pay for.

“Bug bounties are best when transparent and open. The more you try to close them down and place NDAs on them, the less effective they are, the more they become about marketing rather than security,” Robert Graham of Errata Security tells CSO.

Leitschuh, the Zoom bug finder, agrees. “This is part of the problem with the bug bounty platforms as they are right now. They aren’t holding companies to a 90-day disclosure deadline,” he says. “A lot of these programs are structured on this idea of non-disclosure. What I end up feeling like is that they are trying to buy researcher silence.”

The bug bounty platforms’ NDAs prohibit even mentioning the existence of a private bug bounty. Tweeting something like “Company X has a private bounty program over at Bugcrowd” would be enough to get a hacker kicked off their platform.

The carrot for researcher silence is the money — bounties can range from a few hundred to tens of thousands of dollars — but the stick to enforce silence is “safe harbor,” an organization’s public promise not to sue or criminally prosecute a security researcher attempting to report a bug in good faith.

Posted on April 3, 2020 at 6:21 AMView Comments

The Insecurity of WordPress and Apache Struts

Interesting data:

A study that analyzed all the vulnerability disclosures between 2010 and 2019 found that around 55% of all the security bugs that have been weaponized and exploited in the wild were for two major application frameworks, namely WordPress and Apache Struts.

The Drupal content management system ranked third, followed by Ruby on Rails and Laravel, according to a report published this week by risk analysis firm RiskSense.

The full report is here.

Posted on March 18, 2020 at 7:45 AMView Comments

More on Law Enforcement Backdoor Demands

The Carnegie Endowment for International Peace and Princeton University’s Center for Information Technology Policy convened an Encryption Working Group to attempt progress on the “going dark” debate. They have released their report: “Moving the Encryption Policy Conversation Forward.

The main contribution seems to be that attempts to backdoor devices like smartphones shouldn’t also backdoor communications systems:

Conclusion: There will be no single approach for requests for lawful access that can be applied to every technology or means of communication. More work is necessary, such as that initiated in this paper, to separate the debate into its component parts, examine risks and benefits in greater granularity, and seek better data to inform the debate. Based on our attempt to do this for one particular area, the working group believes that some forms of access to encrypted information, such as access to data at rest on mobile phones, should be further discussed. If we cannot have a constructive dialogue in that easiest of cases, then there is likely none to be had with respect to any of the other areas. Other forms of access to encrypted information, including encrypted data-in-motion, may not offer an achievable balance of risk vs. benefit, and as such are not worth pursuing and should not be the subject of policy changes, at least for now. We believe that to be productive, any approach must separate the issue into its component parts.

I don’t believe that backdoor access to encryption data at rest offers “an achievable balance of risk vs. benefit” either, but I agree that the two aspects should be treated independently.

EDITED TO ADD (9/12): This report does an important job moving the debate forward. It advises that policymakers break the issues into component parts. Instead of talking about restricting all encryption, it separates encrypted data at rest (storage) from encrypted data in motion (communication). It advises that policymakers pick the problems they have some chance of solving, and not demand systems that put everyone in danger. For example: no key escrow, and no use of software updates to break into devices).

Data in motion poses challenges that are not present for data at rest. For example, modern cryptographic protocols for data in motion use a separate “session key” for each message, unrelated to the private/public key pairs used to initiate communication, to preserve the message’s secrecy independent of other messages (consistent with a concept known as “forward secrecy”). While there are potential techniques for recording, escrowing, or otherwise allowing access to these session keys, by their nature, each would break forward secrecy and related concepts and would create a massive target for criminal and foreign intelligence adversaries. Any technical steps to simplify the collection or tracking of session keys, such as linking keys to other keys or storing keys after they are used, would represent a fundamental weakening of all the communications.

These are all big steps forward given who signed on to the report. Not just the usual suspects, but also Jim Baker — former general counsel of the FBI — and Chris Inglis: former deputy director of the NSA.

Posted on September 11, 2019 at 6:11 AMView Comments

Computers and Video Surveillance

It used to be that surveillance cameras were passive. Maybe they just recorded, and no one looked at the video unless they needed to. Maybe a bored guard watched a dozen different screens, scanning for something interesting. In either case, the video was only stored for a few days because storage was expensive.

Increasingly, none of that is true. Recent developments in video analytics — fueled by artificial intelligence techniques like machine learning — enable computers to watch and understand surveillance videos with human-like discernment. Identification technologies make it easier to automatically figure out who is in the videos. And finally, the cameras themselves have become cheaper, more ubiquitous, and much better; cameras mounted on drones can effectively watch an entire city. Computers can watch all the video without human issues like distraction, fatigue, training, or needing to be paid. The result is a level of surveillance that was impossible just a few years ago.

An ACLU report published Thursday called “the Dawn of Robot Surveillance” says AI-aided video surveillance “won’t just record us, but will also make judgments about us based on their understanding of our actions, emotions, skin color, clothing, voice, and more. These automated ‘video analytics’ technologies threaten to fundamentally change the nature of surveillance.”

Let’s take the technologies one at a time. First: video analytics. Computers are getting better at recognizing what’s going on in a video. Detecting when a person or vehicle enters a forbidden area is easy. Modern systems can alarm when someone is walking in the wrong direction — going in through an exit-only corridor, for example. They can count people or cars. They can detect when luggage is left unattended, or when previously unattended luggage is picked up and removed. They can detect when someone is loitering in an area, is lying down, or is running. Increasingly, they can detect particular actions by people. Amazon’s cashier-less stores rely on video analytics to figure out when someone picks an item off a shelf and doesn’t put it back.

More than identifying actions, video analytics allow computers to understand what’s going on in a video: They can flag people based on their clothing or behavior, identify people’s emotions through body language and behavior, and find people who are acting “unusual” based on everyone else around them. Those same Amazon in-store cameras can analyze customer sentiment. Other systems can describe what’s happening in a video scene.

Computers can also identify people. AIs are getting better at identifying people in those videos. Facial recognition technology is improving all the time, made easier by the enormous stockpile of tagged photographs we give to Facebook and other social media sites, and the photos governments collect in the process of issuing ID cards and drivers licenses. The technology already exists to automatically identify everyone a camera “sees” in real time. Even without video identification, we can be identified by the unique information continuously broadcasted by the smartphones we carry with us everywhere, or by our laptops or Bluetooth-connected devices. Police have been tracking phones for years, and this practice can now be combined with video analytics.

Once a monitoring system identifies people, their data can be combined with other data, either collected or purchased: from cell phone records, GPS surveillance history, purchasing data, and so on. Social media companies like Facebook have spent years learning about our personalities and beliefs by what we post, comment on, and “like.” This is “data inference,” and when combined with video it offers a powerful window into people’s behaviors and motivations.

Camera resolution is also improving. Gigapixel cameras as so good that they can capture individual faces and identify license places in photos taken miles away. “Wide-area surveillance” cameras can be mounted on airplanes and drones, and can operate continuously. On the ground, cameras can be hidden in street lights and other regular objects. In space, satellite cameras have also dramatically improved.

Data storage has become incredibly cheap, and cloud storage makes it all so easy. Video data can easily be saved for years, allowing computers to conduct all of this surveillance backwards in time.

In democratic countries, such surveillance is marketed as crime prevention — or counterterrorism. In countries like China, it is blatantly used to suppress political activity and for social control. In all instances, it’s being implemented without a lot of public debate by law-enforcement agencies and by corporations in public spaces they control.

This is bad, because ubiquitous surveillance will drastically change our relationship to society. We’ve never lived in this sort of world, even those of us who have lived through previous totalitarian regimes. The effects will be felt in many different areas. False positives­ — when the surveillance system gets it wrong­ — will lead to harassment and worse. Discrimination will become automated. Those who fall outside norms will be marginalized. And most importantly, the inability to live anonymously will have an enormous chilling effect on speech and behavior, which in turn will hobble society’s ability to experiment and change. A recent ACLU report discusses these harms in more depth. While it’s possible that some of this surveillance is worth the trade-offs, we as society need to deliberately and intelligently make decisions about it.

Some jurisdictions are starting to notice. Last month, San Francisco became the first city to ban facial recognition technology by police and other government agencies. A similar ban is being considered in Somerville, MA, and Oakland, CA. These are exceptions, and limited to the more liberal areas of the country.

We often believe that technological change is inevitable, and that there’s nothing we can do to stop it — or even to steer it. That’s simply not true. We’re led to believe this because we don’t often see it, understand it, or have a say in how or when it is deployed. The problem is that technologies of cameras, resolution, machine learning, and artificial intelligence are complex and specialized.

Laws like what was just passed in San Francisco won’t stop the development of these technologies, but they’re not intended to. They’re intended as pauses, so our policy making can catch up with technology. As a general rule, the US government tends to ignore technologies as they’re being developed and deployed, so as not to stifle innovation. But as the rate of technological change increases, so does the unanticipated effects on our lives. Just as we’ve been surprised by the threats to democracy caused by surveillance capitalism, AI-enabled video surveillance will have similar surprising effects. Maybe a pause in our headlong deployment of these technologies will allow us the time to discuss what kind of society we want to live in, and then enact rules to bring that kind of society about.

This essay previously appeared on Vice Motherboard.

Posted on June 14, 2019 at 12:04 PMView Comments

Hacking Construction Cranes

Construction cranes are vulnerable to hacking:

In our research and vulnerability discoveries, we found that weaknesses in the controllers can be (easily) taken advantage of to move full-sized machines such as cranes used in construction sites and factories. In the different attack classes that we’ve outlined, we were able to perform the attacks quickly and even switch on the controlled machine despite an operator’s having issued an emergency stop (e-stop).

The core of the problem lies in how, instead of depending on wireless, standard technologies, these industrial remote controllers rely on proprietary RF protocols, which are decades old and are primarily focused on safety at the expense of security. It wasn’t until the arrival of Industry 4.0, as well as the continuing adoption of the industrial internet of things (IIoT), that industries began to acknowledge the pressing need for security.

News article. Report.

Posted on January 22, 2019 at 5:59 AMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.