Viry a Červi

Google Patches reCAPTCHA Bypass - 29 Květen, 2018 - 18:22
An exploit for the bypass vulnerability required an HTTP parameter pollution in a web application.
Kategorie: Viry a Červi

GCHQ bod tells privacy advocates: Most of our work is making sure we operate within the law

The Register - Anti-Virus - 29 Květen, 2018 - 17:22
'If you whack governments on privacy it will only drive the vulnerability market'

Privacy advocates, journalists and a representative from GCHQ squared off in a debate on surveillance in Cambridge today.…

Kategorie: Viry a Červi

Brazilian Banking Trojan Communicates Via Microsoft SQL Server - 29 Květen, 2018 - 16:47
Researchers have discovered a banking trojan making waves in Brazil with an array of tricks up its sleeve, including using an unusual command and control (C&C) server.
Kategorie: Viry a Červi

BCC is hard, OK? Quite a lot of orgs blurted your email addresses in GDPR mailouts

The Register - Anti-Virus - 29 Květen, 2018 - 16:02
Ad blocker Ghostery, UK councils, vitamin sellers all in the blabtastic mix

Amid the chaos of new European data protection rules coming into force at the end of last week, organisations are apparently struggling to grasp even the most basic of technical challenges, sending out non-blinded emails to their users.…

Kategorie: Viry a Červi

Are your Android apps sending unencrypted data?

Sophos Naked Security - 29 Květen, 2018 - 13:47
This simple setup will help you discover if you've got leaky apps.

Wayback Machine ‘unarchives’ spying website

Sophos Naked Security - 29 Květen, 2018 - 13:40
Who is archiving the web, and what happens when people ask for information to be ‘un-archived’? We may have just found out.

Your Firefox account can now be secured with 2FA

Sophos Naked Security - 29 Květen, 2018 - 12:49
Mozilla is rolling out support for two-factor (or two-step) authentication for anyone who has a Firefox account.

Trojan watch

Kaspersky Securelist - 29 Květen, 2018 - 12:00

We continue to research how proliferation of IoT devices affects the daily lives of users and their information security. In our previous study, we touched upon ways of intercepting authentication data using single-board microcomputers. This time, we turned out attention to wearable devices: smartwatches and fitness trackers. Or more precisely, the accelerometers and gyroscopes inside them.

From the hoo-ha surrounding Strava, we already know that even impersonal data on user physical activity can make public what should be non-public information. But at the individual level, the risks are far worse: these smart devices are able to track the moments you’re entering a PIN code in an ATM, signing into a service, or unlocking a smartphone.

In our study, we examined how analyzing signals within wearable devices creates opportunities for potential intruders. The findings were less than encouraging: although looking at the signals from embedded sensors we investigated cannot (yet) emulate “traditional” keyloggers, this can be used to build a behavioral profile of users and detect the entry of critical data. Such profiling can happen discreetly using legitimate apps that run directly on the device itself. This broadens the capacity for cybercriminals to penetrate victims’ privacy and facilitates access to the corporate network of the company where they work.

So, first things first.

Behavioral profiling of users

When people hear the phrase ‘smart wearables’, they most probably think of miniature digital gadgets. However, it is important to understand that most smartwatches are cyberphysical systems, since they are equipped with sensors to measure acceleration (accelerometers) and rotation (gyroscopes). These are inexpensive miniature microcircuits that frequently contain magnetic field sensors (magnetometers) as well. What can be discovered about the user if the signals from these sensors are continuously logged? More than the owner of the gadget would like.

For the purpose of our study, we wrote a fairly simple app based on Google’s reference code and carried out some neat experiments with the Huawei Watch (first generation), Kingwear KW88, and PYiALCY X200 smartwatches based on the Android Wear 2.5 and Android 5.1 for Smartwatch operating systems. These watches were chosen for their availability and the simplicity of writing apps for them (we assume that exploiting the embedded gyroscope and accelerometer in iOS would follow a similar path).

Logging smartwatch signals during password entry

To determine the optimal sampling frequency of the sensors, we conducted a series of tests with different devices, starting with low-power models (in terms of processor) such as the Arduino 101 and Xiaomi Mi Band 2. However, the sensor sampling and data transfer rates were unsatisfactory — to obtain cross-correlation values that were more or less satisfactory required a sampling frequency of at least 50 Hz. We also rejected sampling rates greater than 100 Hz: 8 Kbytes of data per second might not be that much, but not for hours-long logs. As a result, our app sampled the embedded sensors with a frequency of 100 Hz and logged the instantaneous values of the accelerometer and gyroscope readings along three axes (x, y, z) in the phone’s memory.

Admittedly, getting a “digital snapshot” of a whole day isn’t that easy, because the Huawei watch’s battery life in this mode is no more than six hours.

But let’s take a look at the accelerometer readings for this period. The vertical axis shows the acceleration in m/s2, and the horizontal the number of samples (each corresponds to 10 milliseconds on average). For a complete picture, the accelerometer and gyroscope readings are presented in the graphs below.

Digital profile of a user recorded in one hour. Top — accelerometer signals, bottom — gyroscope signals

The graphs contains five areas in which different patterns are clearly visible. For those versed in kinematics, this graph tells a lot about the user.

The most obvious motion pattern is walking. We’ll start with that.

When the user is walking, the hand wearing the smartwatch oscillates like a pendulum. Pendulum swings are a periodic process. Therefore, if there are areas on the graph where the acceleration or orientation readings from the motion sensor vary according to the law of periodicity, it can be assumed that the user was walking at that moment. When analyzing the data, it is worth considering the accelerometer and gyroscope readings as a whole.

Let’s take a closer look at the areas with the greatest oscillations over short time intervals (the purple areas Pattern1, Pattern3, and Pattern5).

Accelerometer and gyroscope readings during walking

In our case, periodic oscillations of the hand were observed for a duration of 12 minutes (Pattern1, figure above). Without requesting geoinformation, it’s difficult to say exactly where the user was going, although a double numerical integration of the acceleration data shows with an accuracy up to the integration constants (initial velocity and coordinates) that the person was walking somewhere, and with varying characteristic velocity.

Result of the numerical integration of the accelerometer data, which gives an estimate of the user’s movement along the x and y axes in the space of one hour (z-axis displacement is zero, so the graph does not show it)

Note that plotting the Y-axis displacement relative to the X-axis displacement gives the person’s approximate path. The distances here are not overly precise, but they are in the order of thousands of meters, which is actually quite impressive, because the method is very primitive. To refine the distance traveled, anthropometric data can be used to estimate the length of each step (which is basically what fitness trackers do), but we shall not include this in our study.

Approximate path of the person under observation, determined on the basis of numerically integrating the accelerometer data along the X and Y axes

It is more difficult to analyze the less active areas. Clearly, the person was at rest during these periods. The orientation of the watch does not change, and there is acceleration, which suggests that the person is moving by car (or elevator).

Another 22-minute segment is shown below. This is clearly not walking — there are no observable periodic oscillations of the signal. However, we see a periodic change in the acceleration signal envelope along one axis. It might be a means of public transport that moves in a straight line, but with stops. What is it? Some sort of public transportation?

Accelerometer data when traveling on public transport

Here’s another time slice.

Pattern 3, accelerometer data

This seems to be a mixture of short periods of walking (for a few seconds), pauses, and abrupt hand movements. The person is presumably indoors.

Below we interpret all the areas on the graph.

Accelerometer and gyroscope readings with decoding of areas

These are three periods of walking (12, 3, and 5 minutes) interspersed with subway journeys (20 and 24 minutes). The short walking interval has some particular characteristics, since it involved changing from one subway line to another. These features are clearly visible, but our interest was in determining them using algorithms that can be executed on the wearable devices themselves. Therefore, instead of neural networks (which we know to be great at this kind of task), we used a simple cross-correlation calculation.

Taking two walking patterns (Walking1 and Walking2), we calculated their cross-correlation with each other and the cross-correlation with noise data using 10-second signal data arrays.

Experiment             max (cor) Ax max (cor) Ay max (cor) Az max (cor) Wx max (cor) Wy max (cor) Wz Walking1 and Walking2 0.73 0.70 0.64 0.62 0.41 0.83 Walking1 and Noise 0.33 0.30 0.32 0.30 0.33 0.33

Maxima of the functions for cross-correlation of walking patterns with each other and with an arbitrary noise pattern

It can be seen from the table that even this elementary approach for calculating cross-correlation functions allows us to identify the user’s movement patterns within his/her “digital snapshot” with an accuracy of up to 83% (given a very rough interpretation of the correlation). This indicator may not seem that high, but it should be stressed that we did not optimize the sample size and did not use more complex algorithms, for example, principle component analysis, which is assumed to work quite well in determining the characteristic parts of the signal log.

What does this provide to the potential attackers? Having identified the user’s movements in the subway, and knowing the characteristic directions of such movement, we can determine which subway line the user is traveling on. Sure, it would be much easier having data about the orientation of the X and Y axes in space, which could be obtained using a magnetometer. Unfortunately, however, the strong electromagnetic pickup from the electric motors, the low accuracy of determining a northerly direction, and the relatively few magnetometers in smartwatches forced us to abandon this idea.

Without data on the orientation of the X and Y axes in space (most likely, different for individual periods), the problem of decoding the motion trajectory becomes a geometric task of overlaying time slices of known length onto the terrain map. Again, placing ourselves in the attacker’s shoes, we would look for the magnetic field bursts indicate the acceleration/deceleration of an electric train (or tram or trolleybus), which can provide additional information allowing us to work out the number of interim points in the time slices of interest to us. But this too is outside the scope of our study.

Cyberphysical interception of critical data

But what does this all reveal about the user’s behavior? More than a bit, it turns out. It is possible to determine when the user arrives at work, signs into a company computer, unlocks his or her phone, etc. Comparing data on the subject’s movement with the coordinates, we can pinpoint the moments when they visited a bank and entered a PIN code at an ATM.

PIN codes

How easy is it to capture a PIN code from accelerometer and gyroscope signals from a smartwatch worn on the wrist? We asked four volunteers to enter personal PINs at a real ATM.

Accelerometer signals when entering a PIN code on an ATM keypad

Jumping slightly ahead, it’s not so simple to intercept an unencrypted PIN code from sensor readings by elementary means. However, this section of the “accelerometer log” gives away certain information — for example, the first half of the graph shows that the hand is in a horizontal position, while the oscillating values in the second half indicate keys being pressed on the ATM keypad. With neural networks, signals from the three axes of the accelerometer and gyroscope can be used to decipher the PIN code of a random person with a minimum accuracy of 80% (according to colleagues from Stevens Institute of Technology). The disadvantage of such an attack is that the computing power of smartwatches is not yet sufficient to implement a neural network; however, it is quite feasible to identify this pattern using a simple cross-correlation calculation and then transfer the data to a more powerful machine for decoding. Which is what we did, in fact.

Experiment             max (cor) Ax max (cor) Ay max (cor) Az max (cor) Wx max (cor) Wy max (cor) Wz One person and different tries 0.79 0.87 0.73 0.82 0.51 0.81

Maxima of the functions for cross-correlation of PIN entry data at an ATM

Roughly interpreting these results, it is possible to claim 87% accuracy in recovering the PIN entry pattern from the general flow of signal traffic. Not bad.

Passwords and unlock codes

Besides trips to the ATM, we were interested in two more scenarios in which a smartwatch can undermine user security: entering computer passwords and unlocking smartphones. We already knew the answer (for computers and phones) using a neural network, of course, but we still wanted to explore first-hand, so to speak, the risks of wearing a smartwatch.

Sure, capturing a password entered manually on a computer requires the person to wear a smartwatch on both wrists, which is an unlikely scenario. And although, theoretically, dictionaries could be used to recover semantically meaningful text from one-handed signals, it won’t help if the password is sufficiently strong. But, again, the main danger here is less about the actual recovery of the password from sensor signals than the ease of detecting when it is being entered. Let’s consider these scenarios in detail.

We asked four people to enter the same 13-character password on a computer 20 times. Similarly, we conducted an experiment in which two participants unlocked an LG Nexus 5X smartphone four times each with a 4-digit key. We also logged the movements of each participant when emulating “normal” behavior, especially in chat rooms. At the end of the experiment, we synchronized the time of the readings, cutting out superfluous signals.

In total, 480 discrete functions were obtained for all sensor axes. Each of them contains 250-350 readings, depending on the time taken to enter the password or arbitrary data (approximately three seconds).

Signal along the accelerometer and gyroscope axes for four attempts by one person to enter one password on a desktop computer

To the naked eye, the resulting graphs are almost identical; the extremes coincide, partly because the password and mode of entry were identical in all attempts. This means that the digital fingerprints produced by one and the same person are very similar to each other.

Signals along the accelerometer and gyroscope axes for attempts to enter the same password by different people on a desktop computer

When overlaying the signals received from different people, it can be seen that, although the passphrase is the same, it is entered differently, and even visually the extremes do not coincide!

Attempts to enter a smartphone unlock code by two different people

It is a similar story with mobile phones. Moreover, the accelerometer captures the moments when the screen is tapped with the thumb, from which the key length can be readily determined.

But the eye can be deceived. Statistics, on the other hand, are harder to hoodwink. We started with the simplest and most obvious method of calculating the cross-correlation functions for the password entry attempts by one person and for those by different people.

The table shows the maxima of the functions for cross-correlation of data for the corresponding axes of the accelerometer and gyroscope.

Experiment             max (cor) Ax max (cor) Ay max (cor) Az max (cor) Wx max (cor) Wy max (cor) Wz One person 0.92 0.63 0.71 0.55 0.76 0.96 Different persons 0.65 0.35 0.31 0.23 0.37 0.76

Maxima of the functions for cross-correlation of password input data entered by different people on a desktop computer

Broadly speaking, it follows that even a very simple cross-correlation calculation can identify a person with up to 96% accuracy! If we compare the maxima of the cross-correlation function for signals from different people in arbitrary text input mode, the correlation maximum does not exceed 44%.

Experiment             max (cor) Ax max (cor) Ay max (cor) Az max (cor) Wx max (cor) Wy max (cor) Wz One person and different activity 0.32 0.27 0.39 0.26 0.30 0.44

Maxima of the functions for cross-correlation of data for different activities (password entry vs. usual surfing)

Experiment             max (cor) Ax max (cor) Ay max (cor) Az max (cor) Wx max (cor) Wy max (cor) Wz One person 0.64 0.47 0.56 0.41 0.30 0.58 Different persons 0.33 0.40 0.40 0.32 0.38 0.34

Maxima of the functions for cross-correlation of data for an unlock code entered by one person and by different people

Note that the smallest cross-correlation function values were obtained for entering the smartphone unlock code (up to 64%), and the largest (up to 96%) for entering the computer password. This is to be expected, since the hand movements and corresponding acceleration (linear and angular) are minimal in the case of unlocking.

However, we note once more that the computing power available to a smartwatch is sufficient to calculate the correlation function, which means that a smart wearable gadget can perform this task by itself!


Speaking from the information security point of view, we can conclude that, without a doubt, portable cyberphysical systems expand the attack surface for potential intruders. That said, the main danger lies not in the direct interception of input data — that is quite difficult (the most successful results are achieved using neural networks) and thus far the accuracy leaves much to be desired. It lies instead in the profiling of users’ physical behavior based on signals from embedded sensors. Being “smart,” such devices are able to start and stop logging information from sensors not only through external commands, but on the occurrence of certain events or the fulfillment of certain conditions.

The recorded signals can be transmitted by the phone to the attacker’s server whenever the latter has access to the Internet. So an unassuming fitness app or a new watch face from the Google Play store can be used against you, right now in fact. The situation is compounded by the fact that, in addition to this app, simply sending your geotag once and requesting the email address linked to your Google Play account is enough to determine, based on your movements, who you are, where you’ve been, your smartphone usage, and when you entered a PIN at an ATM.

We found that extracting data from traffic likely to correspond to a password or other sensitive information (name, surname, email address) is a fairly straightforward task. Applying the full power of available recognition algorithms to these data on a PC or in cloud services, attackers, as shown earlier, can subsequently recover this sensitive information from accelerometer and gyroscope signal logs. Moreover, the accumulation of these signals over an extended period facilitates the tracking of user movements — and that’s without geoinformation services (such as GPS/GLONASS, or base station signals).

We established that the use of simple methods of analyzing signals from embedded sensors such as accelerometers and gyroscopes makes it possible (even with the computing power of a wearable device) to determine the moments when one and the same text is entered (for example, authentication data) to an accuracy of up to 96% for desktop computers and up to 64% for mobile devices. The latter accuracy could be improved by writing more complex algorithms for processing the signals received, but we intentionally applied the most basic mathematical toolbox. Considering that we viewed this experiment through the prism of the threat to corporate users, the results obtained for the desktop computer are a major cause for concern.

A probable scenario involving the use of wearable devices relates to downloading a legitimate app to a smartwatch — for example, a fitness tracker that periodically sends data packets of several dozen kilobytes in size to a server (for example, the uncompressed “signal signature” for the 13-character password was about 48 kilobytes).

Since the apps themselves are legitimate, we assume that, alongside our Android Wear/Android for Smartwatch test case, this scenario can be applied to Apple smartwatches, too.


There are several indications that an app downloaded onto a smartwatch might not be safe.

  1. If, for instance, the app sends a request for data about the user’s account (the GET_ACCOUNTS permission in Android), this is cause for concern, since cybercriminals need to match the “digital fingerprint” with its owner. However, the app can also allow the user to register by providing an email address — but in this case you are at least free to enter an address different to that of the Google Play account to which your bank card is linked.
  2. If the app additionally requests permission to send geolocation data, your suspicions should be aroused even further. The obvious advice in this situation is not to give additional permissions to fitness trackers that you download onto your smartwatch, and to specify a company email address at the time of registration.
  3. A short battery life can also be a serious cause for concern. If your gadget discharges in just a few hours, this is a sign that you may be under observation. Theoretically, a smartwatch can store logs of your activity with length up to dozens of hours and upload this data later.

In general, we recommend keeping a close eye on smartwatches sported by employees at your office, and perhaps regulating their use in the company’s security policies. We plan to continue our research into cyberphysical systems such as wearable smart gadgets, and the additional risks of using them.

Tuesday review – the hot 22 stories of the week

Sophos Naked Security - 29 Květen, 2018 - 11:44
From the ticking time bomb lurking on your router and Chrome dropping 'secure' label to the forgotten site that attracted hacks and fines, and more!

Ex-staffer of dept bags payout after boss blabbed medical info to colleagues

The Register - Anti-Virus - 29 Květen, 2018 - 11:36
Manchester man wins 'substantial' damages

A Manchester man has won his case against former employer the Department for Work and Pensions, after a superior shared “highly private” medical information with his colleagues.…

Kategorie: Viry a Červi

ISP popped router ports, saving customers the trouble of making themselves hackable

The Register - Anti-Virus - 29 Květen, 2018 - 04:08
SingTel then left them open for a while, because ... well there's no excuse is there?

Singaporean broadband subscribers were left vulnerable to attackers after their ISP opened remote access ports on their gigabit modems and forgot to close them.…

Kategorie: Viry a Červi

Softbank's 'Pepper' robot is a security joke

The Register - Anti-Virus - 29 Květen, 2018 - 02:29
Big-in-Japan 'bot offers root access through hard-coded password and worse bugs too

Softbank's popular anthropomorphic robot, Pepper, has myriad security holes according to research published by Scandinavian researchers earlier this month.…

Kategorie: Viry a Červi

Singapore ISP Leaves 1,000 Routers Open to Attack - 28 Květen, 2018 - 17:02
Telcom firm leaves port open on customer routers after maintenance update exposing hundreds of customers to possible attack.
Kategorie: Viry a Červi

Despite Ringleader’s Arrest, Cobalt Group Still Active - 28 Květen, 2018 - 14:21
The threat actors behind widespread attacks on banks and ATM jackpotting campaigns in Russia and Europe resurfaced in may, attacking banks.
Kategorie: Viry a Červi

WannaCry, rok poté

VIRY.CZ - 28 Květen, 2018 - 13:00

Přibližně před rokem se začal masově šířit ransomware WannaCry. Řádil tak ve velkém, že ho uživatelé mohli „vidět“ na nádražích i billboardech…

Červ i ransomware v jednom

Zatímco většina dnešní havěti sází na tzv. sociální inženýrství, kdy prostě uživatele obelstí a ten si havěť spustí de facto dobrovolně sám, WannyCry byla jiná. Sázela na chybu v operačním systému Microsoft Windows (přesněji ve službě Server Message Block – SMB, port 445). Uživatel tak nemusel pro ztrátu, resp. šifrování souborů dělat nic. Havěť se dokázala sama šířit po firemní síti z jednoho počítače do druhého a všude tam zanechala spoušť – zašifrované soubory, kdy jako výpalné za obnovu dat požadovala 300$, případně 600$ (tj. šlo o ransomware).

Nebezpečná bezpečnostní agentura (NSA)

Tuto chybu přitom dokázal zneužít programový kód (tzv. exploit) s názvem EternalBlue, který využívala americká Národní bezpečnostní agentura (NSA) a používala ho minimálně od roku 2016 ke šmírování. O existující chybě pochopitelně Microsoft neinformovala a minimálně do 14. března 2017 tak šlo o zero-day exploit. Tj. chyba, na kterou neexistuje záplata. Toho dne zveřejnil Microsoft oficiální záplatu (MS17-010).

Exploit EternalBlue byl NSA patrně v roce 2016 ukraden a do internetu se dostal 14. dubna 2017 díky hackerské skupině Shadow Brokers. V polovině května 2017 se pak světem rozletěla havěť WannyCry, která tohoto veřejného objevu využila a díky EternalBlue podpořila svoje masivní šíření. Ač byla záplata dvě měsíce na světě, evidentně byla nainstalovaná na nedostatečném množství počítačů…

Na nádražích, na billboardech…

Výsledek působení havěti WannyCry tak byl vidět například na nádražích:


Nebo na billboardech:

Velkou galerii ze života lze najít například zde.

Bývalý hrdina, který to vypnul

Havěť WannaCry měla i svého hrdinu. Teda chvíli. V kódu totiž existoval tzv. „kill switch“. Tj. přepínač, který dokázal tuto havěť celosvětově vypnout z jednoho místa. Tímto spínačem přitom byla internetová doména s názvem Havěť ověřovala, zda existuje. Pokud existuje, přestane se dále šířit. Této vymoženosti si rychle všiml analytik z Velké Británie a doménu za pár drobných zaregistroval. Stal se tak neznámým hrdinou, než jeho jeho identitu odhalil deník The Guardian. Byl jím Marcus Hutchins. Hrdinou byl ale jen do srpna 2017, kdy byl zatčen na letišti v Las Vegas za to, že se podílel na vývoji a šíření bankovní havěti Kronos v letech 2014 a 2015. V článku „Who Is Marcus Hutchins?“ lze pak najít skvělou detektivní práci a zjistit, že to byl v reálu pěkný vejlupek a to výše uvedené byla patrně jeho jediná světlejší chvíle!

The post WannaCry, rok poté appeared first on VIRY.CZ.

Kategorie: Viry a Červi

2018 Fraud World Cup

Kaspersky Securelist - 28 Květen, 2018 - 12:00

There are only two weeks to go before the start of the massive soccer event — FIFA World Cup. This championship has already attracted the attention of millions worldwide, including a fair few cybercriminals. Long before kick-off, email accounts began bulging with soccer-related spam, and scammers started exploiting the topic in mailings and creating World Cup-themed phishing pages.

Our statistics show spikes in the number of phishing pages during match ticket sales. Every time tickets went on sale, fraudsters mailed out spam and activated clones of official FIFA pages and sites offering fake giveaways allegedly from partner companies. But as the event draws nearer, cyber scams are reaching fever pitch. We present our observations below.

Fake lottery win notifications

One of the main types of World Cup-related email fraud is spam informing recipients of cash winnings in lotteries supposedly held by official partners and sponsors (Visa, Coca-Cola, Microsoft, etc.), as well as FIFA itself.

Examples of fake lottery win notifications

Such messages contain attachments (usually PDF or DOCX documents) in which the “winner” is congratulated and told to forward detailed contact details (name, date of birth, address, email, telephone no.) in order to receive the prize. Sometimes recipients are asked to pay a part of the postage or bank transfer fees.

Such mailouts are aimed primarily at harvesting user data (including financial), plus picking up a small money transfer. Such messages can also contain malicious attachments, for example, Trojan-Banker programs.

Examples of fake notifications with attached documents

Another type of common spam fraud is an offer to take part in a ticket giveaway or win a trip to a match. Victims are required either to register on a fake promotion page and provide an email address, or, as in the case of lottery emails, to send the “organizers” their contact details. Such messages are sent in the name of FIFA, usually from addresses on recently registered domains. The purpose of such schemes is mainly to update email databases so as to distribute yet more spam.

Examples of messages with ticket and trip giveaways

Advertising spam

In the runup to the championship, we registered a lot of advertising spam with offers for soccer merchandise, transport/accommodation services, and travel packages from various tour operators. Merchandise was generally offered by small online retailers and included toys, souvenirs, and stationery marked with official logos, as well as soccer jerseys for all teams taking part. Some messages even resemble mailings from the official FIFA store.

Examples of messages offering merchandise

There were also spammings unrelated to soccer. For example, traditional spam offering medical products, but using the World Cup to attract attention. Interestingly, the message subject referred to the 2006 World Cup final. Perhaps the spammers used an old template and forgot to change the date.

Wrong year, same product

Ticket sales

Besides online stores selling merchandise, there are plenty of sites offering match tickets, both fake and real. But real doesn’t necessarily mean bona fide: they are often sold by ticket scalpers exploiting various loopholes in the FIFA rules.

Online scalpers selling tickets for an arm and a leg

However, official tickets can only be bought on the official FIFA website, and large fines are imposed for their illegal sale or resale. Those who use the services of speculators risk being turned away at the stadium: tickets are personalized, and if the bearer fails to show ID matching the information in the ticket, FIFA staff have the right to refuse entry.

Fake sites and messages from partners

One of the most popular ways to steal credentials for bank and other accounts is to create counterfeit imitations of official partner websites. Partner organizations quite often arrange ticket giveaways for clients, and this is what attackers exploit to lure users onto fake promotion sites. Such pages look very convincing: well-designed with a working interface, hard to tell from the real thing.

Phishing login page supposedly of a partner bank

Attempt to gain access to an account on a partner company site under the guise of a ticket giveaway

Scammers also try to extract data by mimicking official FIFA notifications. The victim is informed that the security system has been updated and all personal data must be re-entered to avoid lockout. The link in the message takes the victim far away from FIFA to a fake personal account. Naturally, all data entered flows straight to the scammers.

Example of a phishing email seemingly from FIFA

Cybercriminals are particularly keen to target clients of Visa, the tournament’s commercial sponsor, and offer prize giveaways in the name of this international payment heavyweight. To take part, users need to follow a link that unsurprisingly points to a phishing site (the domain was registered a couple of months ago and has nothing to do with the payment system), where they are asked to enter their bank card details, including the CVV/CVC code.

Example of a message and phishing page in the name of Visa

Fraud allsorts

Alongside social engineering, phishers deploy malicious programs in the pursuit of users’ personal data and cash. For example, a fake site offering online broadcasts can plant malware on the victim’s computer under the guise of a Flash Player update required to view the match.

In some cases, phishers have no interest at all in bank accounts and payment details. For instance, under the pretext of receiving a World Cup-themed update for the video game FIFA Soccer, users are prompted to enter their account credentials for the Origin platform on a fake login page. If there are games of interest under the victim’s profile, the cybercriminals change the login/password and link the account to a new email address for subsequent resale.

Fake Origin login page

In late May, a few weeks before the start of the championship, phishing emails offering cheap flights from the major airlines were all the rage. In addition to fake soccer ticket giveaways, there were draws seemingly on behalf of airlines offering free plane tickets.

Fake ticket giveaway in the name of a major airline

Tricks of the trade

To make their sites seem credible, cybercriminals register domain names combining the words “world,” “worldcup,” “FIFA,” “Russia,” etc. (for example: worldcup2018, russia2018, fifarussia). Normally, though not always, such domains look unnatural (for instance, and have a non-standard domain extension. So in most cases, a close look at the link in the email or the URL after opening the site should be enough to avoid the bait.

DNS WHOIS data for phishing sites

Likewise with a view to lulling user vigilance, cybercriminals acquire the cheapest SSL certificates available: relevant authorities often fail to verify the existence of the entity acquiring the certificate, meaning that the scammers get the all-important HTTPS in front of their address. To spot a fake, it is enough to look at the domain’s WHOIS data. Scam websites tend to have been registered quite recently and for a short time, and their owners are usually private individuals. What’s more, detailed information about the owner is often hidden.

Besides active domain names, we logged a large number of “sleepers”: on them you might find a placeholder page, if that. Cybercriminals use them as a backup: if one domain is blocked, the site moves to the next.

Examples of backup domain names


The above describes only the most popular scams exploiting the World Cup theme. Nevertheless, it provides a fairly complete picture of how cybercriminals operate and what they want. In addition to the above, we expect shortly to see an explosion of phishing sites offering cheap airline tickets to World Cup host cities, as well as fake mailings supposedly from popular accommodation services with “special offers.”

To avoid being duped, follow these simple rules:

  • Buy tickets only on the official FIFA website or at official ticket offices.
  • For online purchases (not only during the tournament), get a separate bank card and set a spending limit.
  • Do not open links or attachments in emails from unknown senders, even if they seem legitimate.
  • Check the addresses of links in notifications from known services; at the slightest suspicion, do not click, but open the site manually in the browser.
  • To preserve your money and nerves, never buy products advertised in spam.
  • Use the latest security solutions to protect against cyberthreats, and keep the databases up-to-date.

FBI to World+Dog: <i>Please</i>, try turning it off and turning it back on

The Register - Anti-Virus - 28 Květen, 2018 - 03:56
Feds trying to catalogue VPNFilter infections

The FBI has reminded the world it wants us to reboot our routers to try and help it identify VPNFilter-affected routers.…

Kategorie: Viry a Červi

Ghostery’s goofy GDPR gaffe – someone’s in trouble come Monday!

Sophos Naked Security - 27 Květen, 2018 - 03:03
Ever CCed an email you were supposed to BCC? Sure you have! But we bet it wasn't your company's "look how good we are at GDPR" email...

Havěť VPNFilter na půl milionu routerů různých značek!

VIRY.CZ - 25 Květen, 2018 - 23:09

V rámci projektu Cisco Talos byly zveřejněny první informace o nově objevené havěti nazvané VPNFilter. Ta napadá síťová zařízení – routery značek Linksys, MikroTik, Netgear a TP-Link a tvoří z nich botnet.

Vydaná zpráva není stále kompletní, jasné ale je, že běžní uživatelé nemají s touto havětí možnost příliš bojovat, protože cílem útoku je router (tj. na krabičce, kterou jsme připojeni k internetu) a na počítač prostě v žádné typické podobě nedorazí (to až na popud útočníků). Značky routerů jako Linksys, MikroTik, Netgear a TP-Link rozhodně nejsou v našich končinách exotické a pokud zpráva odhaduje 500 tisíc zavirovaných routerů, nelze vyloužit, že se nějaký nachází i na území ČR/SR (ač drtivá většina jich je na území Ukrajiny).

Na druhou stranu, běžní uživatelé nejsou pravděpodobně ani cílem útoku, pouze „přestupní“ stanicí. Havěť VPNFilter totiž monitoruje protokoly Modbus a SCADA, které se používají k řízení ve velkých továrnách. A tu doma asi nemáme

Kategorie: Viry a Červi

VPNFilter EXIF to C2 mechanism analysed

Kaspersky Securelist - 24 Květen, 2018 - 20:00

On May 23 2018, our colleagues from Cisco Talos published their excellent analysis of VPNFilter, an IoT / router malware which exhibits some worrying characteristics.

Some of the things which stand out about VPNFilter are:

  • It has a redundant, multi-stage command and control mechanism which uses three different channels to receive information
  • It has a multi-stage architecture, in which some of the more complex functionality runs only in the memory of the infected devices
  • It contains a destructive payload which is capable of rendering the infected devices unbootable
  • It uses a broken (or incorrect) RC4 implementation which has been observed before with the BlackEnergy malware
  • Stage 2 command and control can be executed over TOR, meaning it will be hard to notice for someone checking the network traffic

We’ve decided to look a bit into the C&C mechanism for the persistent malware payload. As described in the Talos blog, this mechanism has several stages:

  • First, the malware tries to visit a number of gallery pages hosted on photobucket[.]com and fetches the first image from the page.
  • If this fails, the malware tries fetching an image file from a hardcoded domain, toknowall[.]com. This C2 domain is currently sinkholed by the FBI.
  • If that fails as well, the malware goes into a passive backdoor mode, in which it processes network traffic on the infected device waiting for the attacker’s commands.

For the first two scenarios in which the malware successfully receives an image file, a C2 extraction subroutine is called which converts the image EXIF coordinates into an IPv4 address. This is used as an easy way to avoid using DNS lookups to reach the C&C. Of course, in case this fails, the malware will indeed lookup the hardcoded domain (toknowall[.]com). It may be worth pointing that in the past, the BlackEnergy APT devs have shown a preference for using IP addresses for C&C instead of hardcoded domain names, which can be easily sinkholed.

To analyse the EXIF processing mechanism, we looked into the sample 5f358afee76f2a74b1a3443c6012b27b, mentioned in the Talos blog. The sample is an i386 ELF binary and is about 280KB in size.

Unfortunately for researchers, it appears that the galleries used by the malware have been deleted, so the malware cannot use the first C2 mechanism anymore. For instance:

With these galleries unavailable, the malware tries to reach the hardcoded domain toknowall[.]com.
While looking at the pDNS history for this domain, we noticed that it resolved to an IP addresses in France, at OVH, between Jan and Feb 2018:

Interestingly, when visiting this website’s C2 URL, we are presented with a JPG image, suggesting it is still an active C2:

Here’s how it looks when viewed as an image:

When we look into the EXIF data for the picture, for instance using IrfanView, it looks as following:

Filename – update.jpg

  • GPS information: –
  • GPSLatitude – 97 30 -175 (97.451389)
  • GPSLongitude – -118 140 -22 (-115.672778)

How to get the IP out of these? The subroutine which calculates the C2 IP from the Latitude and Longitude can be found at offset 0x08049160 in the sample.

As it turns out, VPNFilter implements an actual EXIF parser to get the required information.

First, it searches for a binary value 0xE1. This makes sense because the EXIF attribute information begins with a tag “0xFF 0xE1”. Then, it verifies that the tag is followed by a string “Exif”. This is the exact data that should appear in a correct header of the Exif tag:

Exif tag
FF E1 Exif tag
xx Length of field
45 78 69 66 00 ‘Exif’
00 Padding

The tag is followed by an additional header:

“Attribute information” header
49 49 (or 4D 4D) Byte order, ‘II’ for little endian (‘MM’ for big endian)
2A 00 Fixed value
xx xx Offset of the first IFD

The data following this header is supposed to be the actual “attribute information” that is organized in so-called IFDs (Image File Directory) that are data records of a specific format. Each IFD consists of the following data:

IFD record
xx xx IFD tag
xx xx Data type
xx xx xx xx Number of data records of the same data type
xx xx xx xx Offset of the actual data, from the beginning of the EXIF

The malware’s parser carefully traverses each record until it finds the one with a tag ’25 88′ (0x8825 little endian). This is the tag value for “GPS Info”. That IFD record is, in turn, a list of tagged IFD records that hold separate values for latitude, longitude, timestamp, speed, etc. In our case, the code is looking for the tags ‘2’ (latitude) and ‘4’ (longitude). The data for latitude and longitude are stored as three values in the “rational” format : two 32-bit values, the first is the enumerator and the second one is the denominator. Each of these three values corresponds to degrees, minutes and seconds, respectively.

Then, for each record of interest, the code extracts the enumerator part and produces a string of three integers (i.e. “97 30 4294967121” and “4294967178 140 4294967274″ that will be displayed by a typical EXIF parser as 1193143 deg 55′ 21.00″, 4296160226 deg 47′ 54.00”). Then, curiously enough, it uses sscanf() to convert these strings back to integers. This may indicate that the GPS Info parser was taken from a third-party source file and used as-is. The extracted integers are then used to produce an actual IP address. The pseudocode in C is as follows:

const char lat[] = "97 30 4294967121"; // from Exif data
const char lon[] = "4294967178 140 4294967274"; // from Exif data
int o1p1, o1p2, o2p1, o3p1, o3p2, o4p1;
uint8_t octets[4];

sscanf(lat, "%d %d %d", &o1p2, &o1p1, &o2p1);
sscanf(lon, "%d %d %d", &o3p2, &o3p1, &o4p1);
octets[0] = o1p1 + ( o1p2 + 0x5A );
octets[1] = o2p1 + ( o1p2 + 0x5A );
octets[2] = o3p1 + ( o3p2 + 0xB4 );
octets[3] = o4p1 + ( o3p2 + 0xB4 );

printf("%u.%u.%u.%u\n", octets[0], octets[1], octets[2], octets[3]);

The implementation of the EXIF parser appears to be pretty generic. The fact that it correctly handles the byte order (swapping the data, if required) and traverses all EXIF records skipping them correctly, and that the GPS data is converted to a string and then back to integers most likely indicates that the code was reused from an EXIF-parsing library or toolkit.

For the values provided here, the code will produce the IP address “” that is a known C&C of VPNFilter.

It should be noted that this IP is included in Cisco Talos’ IOCs list as a known C&C. Currently, it appears to be down.

What’s next?

Perhaps the most interesting question is who is behind VPNFilter. In their Affidavit for sinkholing the malware C2, FBI suggests it is related to Sofacy:

Interestingly, the same Affidavit contains the following phrase: “Sofacy Group, also known as apt28, sandworm, x-agent, pawn storm, fancy bear and sednit”. This would suggest that Sandworm, also known as BlackEnergy APT, is regarded as subgroup of Sofacy by the FBI. Most threat intel companies have held these groups separate before, although their activity is known to have overlapped in several cases.

Perhaps the most interesting technical detail, which Cisco Talos points in their blog linking VPNFilter to BlackEnergy, is the usage of a flawed RC4 algorithm.The RC4 key scheduling algorithm implementation from these is missing the typical “swap” at the end of the loop. While rare, this mistake or perhaps optimization from BlackEnergy, has been spotted by researchers and described publicly going as far back as 2010. For instance, Joe Stewart’s excellent analysis of Blackenergy2 explains this peculiarity.

So, is VPNFilter related to BlackEnergy? If we are to consider only the RC4 key scheduling implementation alone, we can say there is only a low confidence link. However, it should be noted that BlackEnergy is known to have deployed router malware going back as far as 2014, which we described in our blogpost: “BE2 custom plugins, router abuse, and target profiles“. We continue to look for other similarities which could support this theory.

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