RESEARCH | August 14, 2014

Remote survey paper (car hacking)

Good Afternoon Interwebs,
Chris Valasek here. You may remember me from such nature films as “Earwigs: Eww”.
Charlie and I are finally getting around to publicly releasing our remote survey paper. I thought this went without saying but, to reiterate, we did NOT physically look at the cars that we discussed. The survey was designed as a high level overview of the information that we acquired from the mechanic’s sites for each manufacturer. The ‘Hackability’ is based upon our previous experience with automobiles, attack surface, and network structure.
Enjoy!
EDITORIAL | August 5, 2014

Upcoming Blackhat & DEF CON talk: A Survey of Remote Automotive Attack Surfaces

Hi Internet,

Chris Valasek here; you may remember me from such movies as ‘They Came to Burgle Carnegie Hall’. In case you haven’t heard, Dr. Charlie Miller and I will be giving a presentation at Black Hat and DEF CON titled ‘A Survey of Remote Automotive Attack Surfaces’. You may have seen some press coverage on Wired, CNN, and Dark Reading several days ago. I really think they all did a fantastic job covering what we’ll be talking about.

We are going to look at a bunch of cars’ network topology, cyber physical features, and remote attack surfaces. We are also going to show a video of our automotive intrusion prevention/detection system.

While I’m sure many of you want find out which car we think is most hackable (and you will), we don’t want that to be the focus of our research. The biggest problem we faced while researching the Toyota Prius and Ford Escape was the small sample set. We were able to dive deeply into two vehicles, but the biggest downfall was only learning about two specific vehicles.

Our research and presentation focus on understanding the technology and implementations, at a high level, for several major automotive manufacturers. We feel that by examining how different manufacturers design their automotive networks, we’ll be able to make more general comments about vehicle security, instead of only referencing the two aforementioned automobiles.

I hope to see everyone in Vegas and would love it if you show up for our talk. It’s at 11:45 AM in Lagoon K on Wednesday August 6.

— CV

P.S. Come to the talk for some semi-related, never-before-seen hacks.

RESEARCH | July 31, 2014

Hacking Washington DC traffic control systems

This is a short blog post, because I’ve talked about this topic in the past. I want to let people know that I have the honor of presenting at DEF CON on Friday, August 8, 2014, at 1:00 PM. My presentation is entitled “Hacking US (and UK, Australia, France, Etc.) Traffic Control Systems”. I hope to see you all there. I’m sure you will like the presentation.

I am frustrated with Sensys Networks (vulnerable devices vendor) lack of cooperation, but I realize that I should be thankful. This has prompted me to further my research and try different things, like performing passive onsite tests on real deployments in cities like Seattle, New York, and Washington DC. I’m not so sure these cities are equally as thankful, since they have to deal with thousands of installed vulnerable devices, which are currently being used for critical traffic control.

The latest Sensys Networks numbers indicate that approximately 200,000 sensor devices are deployed worldwide. See http://www.trafficsystemsinc.com/newsletter/spring2014.html. Based on a unit cost of approximately $500, approximately $100,000,000 of vulnerable equipment is buried in roads around the world that anyone can hack. I’m also concerned about how much it will cost tax payers to fix and replace the equipment.

One way I confirmed that Sensys Networks devices were vulnerable was by traveling to Washington DC to observe a large deployment that I got to know.

When I exited the train station, the fun began.

PRESENTATION | July 30, 2014

DC22 Talk: Killing the Rootkit

By Shane Macaulay

I’ll  be at DefCon22 a to present information about a high assurance tool/technique that helps to detect hidden processes (hidden by a DKOM type rootkit).  It works very well with little bit testing required (not very “abortable” http://takahiroharuyama.github.io/blog/2014/04/21/memory-forensics-still-aborted/). The process  also works recursively (detect host and guest processes inside a host memory dump).
Plus, I will also be at our IOAsis (http://ioasislasvegas.eventbrite.com/?aff=PRIOASIS) , so come through for a discussion and a demo.
WHITEPAPER | July 1, 2014

A Survey of Remote Automotive Attack Surfaces

By looking at each car’s remote attack surface, internal network architecture, and computer controlled features, we are able to draw some conclusions about the suitability of the vehicle to remote attack. This doesn’t mean that the most susceptible looking isn’t in fact quite secure (i.e. coded very securely) or that the most secure looking isn’t in fact trivially exploitable, but it does provide some objective measure of the security of a large number of vehicles that wouldn’t be possible to examine in detail without a massive effort. It also provides an outline on how to design and construct secure vehicles, namely in making each of these three stages of exploitation as difficult as possible.

The authors also discuss different strategies to securing vehicles from remote attack in a layered, attack resilient fashion. In particular, it introduces a device that acts like a network intrusion detection and prevention device as well as discusses some early testing results.

Lastly, to the authors’ knowledge, this is the first publicly available resource for automotive network architecture review. While network architecture review is commonplace in modern network/computer security, much of automobile topology has been shrouded in secrecy.

(more…)

ADVISORIES |

Belkin WeMo Home Automation Vulnerabilities

The WeMo devices connect to the Internet using the STUN/TURN protocol. This gives users remote control of the devices and allows them to perform firmware updates from anywhere in the world. A generated GUID is the primary source of access control.

WeMo also uses a GPG-based, encrypted firmware distribution scheme to maintain device integrity during updates. Unfortunately, attackers can easily bypass most of these features due to the way they are currently implemented in the WeMo product line. The command for performing firmware updates is initiated over the Internet from a paired device. Also, firmware update notices are delivered through an RSS-like mechanism to the paired device, rather than the WeMo device itself, which is distributed over a non-encrypted channel. As a result, attackers can easily push firmware updates to WeMo users by spoofing the RSS feed with a correctly signed firmware. (more…)

ADVISORIES |

Steam Client Creates World-writable Shell Script

While performing a routine world-writable file scan, one of IOActive’s consultants discovered that the Steam Client for Mac OS X creates world-writable shell scripts when installing games. (more…)

ADVISORIES |

OleumTech Wireless Sensor Network Vulnerabilites

OleumTech has manufactured industrial wireless solutions for almost 15 years, providing visibility to disparate assets for major Oil & Gas producers for near real-time optimization decisions, resource deployment, and regulatory compliance. OleumTech also manufacturers industrial automation systems that represents the new paradigm of remote monitoring and control for industries, such as Oil & Gas, Refining, Petro-chemical, Utilities, and Water/Wastewater.

In June 2013, IOActive Labs reported four critical vulnerabilities in OleumTech’s wireless sensor network to ICS-CERT. To date, IOActive Labs is not aware of any fixes released by OleumTech. (more…)

PRESENTATION | June 16, 2014

Video: Building Custom Android Malware for Penetration Testing

By Robert Erbes  @rr_dot 
 
In this presentation, I provide a brief overview of the Android environment and a somewhat philosophical discussion of malware. I also take look at possible Android attacks in order to help you pentest your organization’s defenses against the increasingly common Bring Your Own Device scenario.

INSIGHTS | May 7, 2014

Glass Reflections in Pictures + OSINT = More Accurate Location

By Alejandro Hernández – @nitr0usmx

Disclaimer: The aim of this article is to help people to be more careful when taking pictures through windows because they might reveal their location inadvertently. The technique presented here might be used for many different purposes, such as to track down the location of the bad guys, to simply know in which hotel is that nice room or by some people, to follow the tracks of their favorite artist.
All of the pictures presented here were posted by the owners on Twitter. The tools and information used to determine the locations where the pictures were taken are all publically available on the Internet. No illegal actions were performed in the work presented here. 

 
 
Introduction
Travelling can be enriching and inspiring, especially if you’re in a place you haven’t been before. Whether on vacation or travelling for business, one of the first things that people usually do, including myself, after arriving in their hotel room, is turn on the lights (even if daylight is still coming through the windows), jump on the bed to feel how comfortable it is, walk to the window, and admire the view. If you like what you see, sometimes you grab your camera and take a picture, regardless of reflections in the window.
Without considering geolocation metadata [1] (if enabled), reflections could be a way to get more accurate information about where a picture was taken. How could one of glass’ optical properties [2], reflection, disclose your location? Continue reading.
Of course pictures taken from windows disclose location information such as the city and/or streets; however, people don’t always disclose the specific name of the place they’re standing. Here is where reflections could be useful.
Sometimes, not all of the time, but sometimes, reflections contain recognizable elements that with a little extra help, such as OSINT (Open Source Intelligence) [3], could reveal a more accurate location. The OSINT elements that I used include:
       Google Earth 3D Buildings (http://www.google.com/earth/)
       Google Maps (and Street View) (http://maps.google.com)
       Emporis (buildings information) (http://www.emporis.com)
       SkyscraperPage (buildings information) (http://skyscraperpage.com)
       Foursquare (pictures uploaded by people) (http://foursquare.com)
       TripAdvisor (pictures uploaded by people) (http://www.tripadvisor.com)
       Hotels’ Websites
       Google.com
In the following case studies, I’ll present step-by-step instructions for how to get more accurate information about where a picture was taken using reflections.
CASE #1 – Miami, FL
Searching for “hotel view” pictures on Twitter, I found this one from Scott Hoying (a member of Pentatonix, an a cappella group of five vocalists):
 
Looking at his previous tweet:
 
 We know he was in Miami, but, where exactly? Whether or not you’ve been to Miami, it’s difficult to recognize the buildings outside the window:
 
 So, I went to Wikipedia to get the name of every member of the band:
I looked for them on Twitter and Instagram. Only one other member had published a picture from what appeared to be the same hotel:
I was relatively easy to find that view with Google Earth:
However, from that perspective, there are three hotels:
So, it’s time to focus on the reflection elements present in the picture (same element reflected in different angles and the portraits) as well as the pattern in the bed cover:
Two great resources for reference pictures, in addition to hotels’ websites, are Foursquare and TripAdvisor (some people like to show where they’re staying). So, after a couple of minutes analyzing pictures of the three possible hotels, I finally found our reflected elements in pictures uploaded by people and on the hotel’s website itself:
After some minutes, we can conclude that the band stayed at the Epic Hotel and, perhaps, in a Water View suite:
 
 
CASE #2 – Vancouver, Canada
The following picture was posted by a friend of mine with the comment that she was ready for her talk at #XXXX2014 conference in #Vancouver. The easiest way to get her location would have been to look for the list of partnering hotels for speakers at XXXX2014 conference, but what if the name of the conference hadn’t been available?
Let’s take the longer but more interesting path, starting with only the knowledge that this picture was taken in Vancouver:
The square lamp reflected is pretty clear, isn’t it? ;-). First, let’s find that building in front. For that, you could go straight to Google Earth with the 3D buildings layout enabled, but I preferred to dive into Vancouver’s pictures in Emporis:
We’ve found it’s the Convention Centre (and its exact location evidently). Now, it’s easy to match the perspective from which the picture was taken using Google Earth’s 3D buildings layout:
We see it, but, where are we standing? Another useful OSINT resource I used was the SkyscraperPage of Vancouver, which shows us two options:
 
By clicking on each mark we can see more detailed information. According to this website, the one on the right is used only for offices and retail, but not for lodging: 
However, the other one seems to be our building:
A quick search leads us to the Fairmont Pacific Rim’s Website, where it’s easy to find pictures from inside the hotel:
The virtual tour for the Deluxe Room has exactly the same view:
Turn our virtual head to the left… and voilà, our square lamp:
Now, let’s find out how high up it is to where the picture was taken. Let’s view our hotel from the Convention Center’s perspective and estimate the floor:
From my perspective, it appears to be between the 17th and 20th floor, so I asked the person who took the picture to corroborate:
 
CASE #3 – Des Moines, IA – 1
An easy one, there are not many tall buildings in Des Moines, Iowa, so it was sort of easy to spot this one:
It seems that the building in front is a parking garage. The drapes look narrow and are white / pearl color. The fans on the rooftop were easy to locate on Google Maps:
And we could corroborate using the Street View feature:
We found it was the Des Moines Marriott Downtown. Looking for pictures on TripAdvisor we’ve found what it seems to be the same drapery:
Which floor? Let’s move our head and look towards the window where the picture was taken:
The 3D model also helps:
And… almost!
CASE #4 – Des Moines, IA – 2
Another easy case from the same hotel as the previous case. Look at the detailed reflections: the beds, the portraits, the TV, etc.
These were easy-to-spot elements using Foursquare and TripAdvisor:
Misc. Ideas / Further Research
While brainstorming with my friend Diego Madero about reflections, he suggested going deeper by including image processing to separate the reflections from the original picture. It’s definitely a good idea; however, it takes time to do this (as far as I know).
We also discussed the idea that you could use the information disclosed in reflections to develop a profile of an individual. For example, if the person called room service (plates and bottles reflected), what brand of laptop they are using (logo reflected), or whether they are storing something in the safe (if it’s closed or there’s an indicator like an LED perhaps).
Conclusion
Clear and simple: the reflected images in pictures might disclose information that you wouldn’t be willing to share, such as your location or other personal details. If you don’t want to disclose your location, eliminate reflections by choosing a better angle or simply turning off all of the lights inside the room (including the TV) before taking the picture.
Also, it’s evident that reflections are not only present in windows. While I only considered reflections in windows from different hotels, other things can reflect the surrounding environment:
       “44 Impressive Examples of Reflection Photography”
Finally, do not forget that a reflection could be your enemy.
Recommendations
Here are some other useful links:
       “How to Eliminate Reflections in Glasses in Portraits”
       “How to remove the glare and brightness in an image (Image preprocessing)”
Thanks for reading.
References:
[1] “Geolocation”
[2] “Glass – Optical Properties”
[3] “OSINT – Open Source Intelligence”