SPECIAL EDITION

Special Edition is the blog for security testing business SE Labs. It explains how we test security products, reports on the internet threats we find and provides security tips for businesses, other organisations and home users.

Wednesday, 15 February 2017

17 Things Spammers Get Wrong


No one publishes successful phishing and ransomware emails. Jon Thompson thinks he knows why.

The headlines say phishing scams are at an all-time high, and ransomware is growing exponentially, but conspicuous by their absence are examples of the emails behind successful attacks. It's becoming the cliché in the room, but there may be a reason: embarrassment.


Running an email honeypot network, you receive a flood of malicious email every day. Most is littered with glaring errors that point to lazy, inarticulate crooks trying to make the quickest buck from the least effort. When you do come across a rare, well though-out campaign, it shines like a jewel in a sea of criminal mediocrity.

To the average spammer, however, it's all just a numbers game. He cranks the handle on the botnet, so to speak, and money comes out.

This poses an important question: why, given the quality of most malicious spam, are new ransomware infections and high profile phishing attacks still making headlines almost every single day? Clearly, we're massively overestimating the amount of effort and intelligence invested by spammers.

With that in mind, what follows is a short list of 17 mistakes I routinely see, all of which immediately guarantee that an email is malicious. There are others, but these are the main ones. If this list reflects the mistakes found in the spam behind the headlines, then the size yet lack of sophistication of the problem should become apparent.

1.    No Subject Header

This error is particularly prevalent in ransomware campaigns. Messages whose payloads have very low VirusTotal scores are being sent with no subject header. Maybe the sender thinks it'll pique the curiosity of the recipient, but it should also alert spam filters even before they examine the attachment.

2.    No Set Dressing

Look at any real communication from a bank, PayPal, a store, etc. It is well formatted, the HTML is clean, the language is clear, and the branding is obvious. Legitimate companies and banks don’t tend to send important messages in plain text.

3.    Generic Companies

Generic companies are rare but I do occasionally see them. Who is "the other financial institution" and why has it refused my transaction? Vague, instantiated company names like this, with an accompanying attachment, are clear indicators of spam.

4.    Multiple Recipients

This is another example of laziness on the part of spammers. OK, they may have found an open relay to willingly spread messages rather than buy extra time on a botnet, but anything other than a one-to-one sender to recipient ratio should be an instant red flag.

5.    Poor Salutation

Much apparently personalised spam doesn’t use a competent salutation, or uses a salutation that is simply the user name part of the email address (i.e.: "Dear fred.smith"). It would take effort to code a script that personalises the messages by stripping off the first name and capitalising the initial. Effort is the enemy of the fast buck.

6.    No Body Text

Sending an email with a tantalizing subject header such as "Overdue – Please Respond!" but no body text explaining what or why it's overdue is as common in commodity ransomware as having no subject header. The attack again relies entirely on the natural curiosity of the recipient, who can and should simply ignore it. Spam filters should also take a keen interest.

7.    Auto-translated Body Text

Machine translation has the amusing habit of mapping the grammar of one language onto another, resulting in errors that no native speaker would ever make. Manual translation by a highly fluent speaker is far superior to machine translation, but the translator must also have knowledge of the subject matter for his text to appear convincing. Again, this is effort.


8.    The Third Person

This is a great example of a spam writer trying to distance himself from his crime. "PayPal has detected an anomaly in your account" and "they require you to log in to verify your account" just look weird in the context of a security challenge. This is supposed to be from PayPal, isn't it?

9.    Finger Trouble

I'm fast concluding that some cybercriminals really do wear thick leather gloves while typing, just like in the pictures. Either that or they're blind drunk. Random punctuation marks and extra characters that look like they've been hit at the same time as the correct ones don't make a good impression. Simply rejecting emails that have more than a certain percentage of spelling mistakes might prevent many of these messages from getting through.


10.    Unexpected Plurals and Tenses

Using "informations" instead of "information" is a dead giveaway for spam and should be blocked when in combination with other indicators. Phrases such as "we detect a problem" instead of "we detected a problem" also stick out a mile.


11.    Missing Definite Article

Many spam emails stand out as somehow "wrong" because they miss out the definite article. One recent example I saw read: "Access is blocked because we detect credit card linked to your PayPal account has expired." An associated Yandex.ru return address gave the whole thing a distinct whiff of vodka.

12.    The Wrong Word

"Please review the document and revert back to us immediately". Revert? Really? Surely, you mean "get back", not "revert back". It may be difficult for spam filters to weed out this kind of error, but humans should spot it without difficulty.

13.    Misplaced Emphasis

Unusually capitalised phrases such as "You must update Your details to prevent Your Account from being Suspended" look weird. Initial capitalisation isn't used for emphasis in English sentences, and hints at someone trying to make the message sound more official and urgent than it is.






14.    Tautological Terrors

"It is extremely mandatory that you respond immediately". Not just mandatory but extremely mandatory? Wow, I'd better click that link right away! Urgent calls to action like this overplay the importance of the message in ways that mark them out as fake.








15.    Grandiosity

Using grand words where normal ones should appear to make a message sound more authoritative are a dead giveaway.  Here's an example from last September when a gang famously tried to distribute malware on the back of a new media player release: "To solemnise the release of our new software". Solemnise means to mark with a formal ceremony.

What they really meant was: "To mark the release of our new software".  The whole message was also riddled with the most outrageous auto-translate errors that it made difficult reading.

16.    Overly-grand Titles

Why would the Microsoft Chief Support Manager be contacting me personally all the way from the US to give me a refund? Wouldn't he delegate this important work to a local minion? Similarly, the head of the IMF doesn’t usually spend their days emailing strangers about ATM cards stacked high with cash.


17.    Obfuscated URLs

If the collar doesn't match the cuffs, it's a lie. In other words, if the message contains the name of a high-street bank (for example) and a URL from a shortening service such as bit.ly, spam filters should be blocking the message without question, regardless of the rest of the content.



Friday, 27 January 2017

Developer claims anti-virus does not improve security

Anti-virus is bad, dead (again) and worse, its corpse is poisoning the ecosystem of good software.

There is, according to former Mozilla developer Robert O'Callahan, negligible evidence that anti-malware software produced by third-parties provides any additional security. His arguments have spread from his blog to Twitter and then to IT news websites like IT Pro and The Register.

We test anti-malware software and have, as a team, being doing so for years. We think we have plenty of strong evidence that third-party anti-malware software provides improved security over that which comes with Windows by default. Our enterprise, small business and consumer reports are free to download.

There is no doubt that updating your operating system makes it more secure. We've run tests to prove that this oft-quoted advice is based on real, reproducible data. But what we've also seen is that adding a decent anti-virus package to a good patching schedule raises protection levels even higher.

There is a difference

To say that all anti-virus software is equally (in)effective is just plain wrong, and there are plenty of results from different testing labs that show this. You may not trust all of those labs, and you may have problems with some (or all) of the ways that they test, but I would strongly suggest that we can't all be wrong.

Our position on the Microsoft anti-malware included with Windows is that it is far better than it used to be, but that some commercial third-party packages are consistently stronger.

Why do people bash 'anti-virus' all the time?

Different individuals and companies have axes to grind when it comes to anti-virus or, to use a more modern and appropriate term, 'anti-malware' software.

  • New anti-malware vendors sometimes disparage more established vendors as providing less sophisticated products as a marketing tool.
  • Windows developers at Microsoft don't like the perception (which is sometimes the truth) that anti-malware products slow down Windows. When a user has a bad Windows experience, for whatever reason, Microsoft feels the impact.
  • Other developers hate that anti-malware products embed themselves into Windows in sometimes strange and unusual ways, causing potential havoc with their own efforts and possibly introducing new and powerful security vulnerabilities. Anti-malware vendors argue that they need to do this to prevent particularly nasty threats from digging in at the lowest security levels within the operating system.
  • Users who have never (knowingly) suffered a malware attack often question the very necessity for anti-malware.
  • Some testers/ researchers make it their life's mission to discover technical problems with anti-malware, sometimes apparently taking the position that "anti-malware is bad for you," rather than, "you need it, it's a bit broken but here's how to fix it."

So is anti-virus the ultimate solution?

I have never seen a perfect anti-malware product, in terms of the protection that it offers, the performance impact that it makes and the additional attack surface that it exposes. But nor have I encountered a perfect operating system, browser or user.

We can throw away our anti-malware software when our operating systems are fully secure; we, as users, stop clicking on links to malware; and criminals and other 'agencies' stop attacking our computers en-masse.

Tuesday, 10 January 2017

How well does your anti-virus really protect you?

Not equally well, is the short answer. Find out which products are consistently the best.

Latest reports now online

Welcome to the final set of endpoint security tests for 2016. We've spent the entire year scanning the internet for prevalent threats that affect real people and exposing popular security products to those same threats in real-time.

If you want an answer to the question, "How well does my anti-malware product protect me?" the reports we've published throughout the year should go some way to helping you either feel safe or make an informed decision on which product to change to. You can find these, and earlier reports, on our website.

But helping you, our readers, choose the best products is only part of our mission. We want products to improve, because even the best are not perfect. We offer the developers of these products the chance to engage with us and learn where the problems lie. At the end of each test we could say to them, "bad luck, you missed these threats. Better luck next time!"

But what we do is provide a huge amount of useful data to companies that want to work with us. This includes extremely detailed analyses of the threat itself, how it worked against individual products and forensic data proving what happened.

This data provides two benefits to the security companies: the first is proof that we're not just making everything up! The second is an unbiased, third-party quality assurance service that can identify problems overlooked by in-house teams. In the end they benefit and so do you, if you use their products.

We're trying to make things better. Thanks for your support throughout the year.

Our latest reports, for enterprise, small business and home users are now available for free from our website. Please download them and follow us on Twitter to receive updates and future reports.

Thursday, 5 January 2017

Predictions for 2017

Still dazed from the year that was, Jon Thompson dons his Nostradamus hat, dusts off his crystal ball
and stares horrified into 2017.


Prediction is difficult. Who would have thought a year ago that ransomware would now come with customer care, or that Russia would be openly accused of hacking a bombastic businessman into the Whitehouse. Who even dreamed Yahoo would admit to a billion-account compromise?

So, with that in mind, it's time to gaze into the abyss and despair…

Let's get the obvious stuff out of the way first. Mega credential breaches won't go away. With so many acres of forgotten code handling access to back end databases, it's inevitable that the record currently held by Yahoo for the largest account breach will be beaten.

Similarly, ransomware is only just beginning. Already a billion-dollar industry, it's cheap to buy into and easy to profit from. New techniques are already emerging as gangs become more sophisticated. First came the audacious concept of customer service desks to help victims through the process of forking over the ransom. By the end of 2016, the Popcorn Time ransomware gang was offering decryption for your data if you infect two of your friends who subsequently pay up. With this depth of innovation already in place, 2017 will hold even greater horrors for those who naively click attachments.

Targeted social engineering and phishing attacks will also continue to thrive, with innovative
campaigns succeeding in relieving companies of their revenues. Though most untargeted bulk phishing attempts will continue to show a low return, phishers will inevitably get wise and start to make their attacks more believable. At SE Labs, we've already seen evidence of this.

It's also obvious that the Internet of Things will continue to be outrageously insecure, leading to DDoS attacks that will make the 1.1Tbps attack on hosting company OVH look trivial. The IoT will also make ransomware delivery even more efficient, as increasing armies of compromised devices pump out the pink stuff. By the end of 2017, I predict hacking groups (government-backed or otherwise) will have amassed enough IoT firepower to knock small nations offline. November's test of a Mirai botnet against Liberia was a prelude to the carnage to come.

Bitcoin  recently passed the $1,000 mark for the first time in three years, which means criminals will want even more than ever to steal the anonymous cryptocurrency. However, a flash crash in value is also likely as investors take profits and the market panics in response to a sudden fall. It's happened before, most noticeably at the end of 2013. There's also the distinct possibility that the growth in value is due to ransomware, in which case the underlying rally will continue regardless of profit takers.

The state-sponsored use of third party hacking groups brings with it plausible deniability, but proof cannot stay hidden forever. One infiltration, one defection, one prick of conscience, and someone will spill the beans regardless of the personal cost. It's highly likely that 2017 will include major revelations of widespread state-sponsored hacking.

This leads me neatly on to Donald Trump and his mercurial grasp of "the cyber". We've already delved into what he may do as president, and much of what we know comes straight from the man himself. For example, we already know he skips his daily security briefings because they are "repetitive", and prefers to ask people around him what's going on because "You know, I'm, like, a smart person."

Trump's insistence on cracking down on foreign workers will have a direct impact on the ability of the US to defend itself in cyberspace. The shift from filling jobs with overseas expertise to training homegrown talent has no discernible transition plan. This will leave a growing skills gap for several years as new college graduates find their way to the workplace. This shortfall will be exploited by foreign threat actors.

Then there's Trump's pompous and wildly indiscreet Twitter feed. Does the world really need to know when secret security briefings are postponed, or what he thinks of the intelligence presented in those meetings? In espionage circles, everything is information, and Trump needs to understand that. I predict that his continued use of social media will lead to internal conflict and resignations this year, as those charged with national cybersecurity finally run out of patience.

It's not all doom and gloom, however. The steady development of intelligent anti-spam and anti-malware technologies will see a trickledown from advanced corporate products into the hotly contested consumer market. The first AV vendor to produce an overtly next gen consumer product will change the game – especially if a free version is made available.

There's also a huge hole in "fake news" just begging to be filled. I predict that 2017 will see the establishment of an infosec satire site. Just as The Onion has unwittingly duped lazy journalists in the past, there's scope for the same level of hilarity in the cybersecurity community.

However, by far the biggest threat to life online in 2017 will continue to be the end user. Without serious primetime TV and radio campaigns explicitly showing exactly what to look for, users will continue to casually infect themselves and the companies they work for with ransomware, and to give up their credentials to phishing sites. When challenged, I also predict that governments will insist the problem is being addressed.

So, all in all, it's business as usual.

Happy 2017!


Monday, 5 December 2016

How To Really Stop Phishing


If phishing sites want data, they'll get it!

Running a honeypot, you soon realise there are four types of spam. The first is basically just adverts. Next comes social engineering spam, which is mostly advanced fee fraud. There's a ton of cash or a pretty girl waiting if you send a small processing fee. By far the largest category is ransomware, but this is closely followed by that perennial favourite, phishing spam.

Phishing works. Its "product" nets huge profits in two ways. First, by direct use of the stolen data. Second, from sales of that data to other criminals. This got me thinking about how to fight back.

Phishing sites tend to be static replicas of the real thing, with a set of input boxes and a submit button. That is their major weakness. Another is that, though the inputs might be scrubbed to remove the possibility of a sneaky SQL injection, the information being entered might not be checked. Who's to say that the date of birth, password, bank details etc. that you enter are real? What if you were to enter a thousand different sets of bogus information? How about a million, or even ten million?


What I propose is that when a phishing site is discovered, it would be fun to deploy a script to flood it with random data of the appropriate format for each input field. Finding real data in the collected noise would become nearly impossible, and so would help protect the innocent. If such poor-quality data is sold on to third parties, then Mr Big will soon want his money back and probably a lot more besides.

Diluting phished data to homeopathic strengths is one thing, but the general idea could be applied in other ways. One of the main tasks in running a spam honeypot is "seeding". This involves generating email addresses to accidentally-on-purpose leave in plain sight for later harvesting by spammers. If someone were to set up a honeypot with a huge number of domains pointing to it, and with a huge number of active login accounts, those accounts can be leaked or even sold (with all profits going to charity, naturally!) as being demonstrably live and real. If the buyer tests any of them, they'll work. Set up the honeypot in enough interesting detail, and Mr Big won't be able to tell he's been duped for quite some time.

Phishing is popular because it's easy, relatively safe for the perpetrator, and highly profitable. Frustrating the efforts of criminals, casting doubt on the phished data being sold, and hopefully causing wars between cybergangs is certainly one potentially very entertaining way of fighting back.

Of course, flooding phishing sites with bogus data may already be quietly happening. I certainly hope so…


Monday, 28 November 2016

What is Machine Learning?

What is machine learning, and how do we know it works?

What's the difference between artificial intelligence and machine learning? Put simply, artificial intelligence is the area of study dedicated to making machines solve problems that humans find easy but digital computers find hard, such as driving cars, playing chess or recognising sarcasm. Machine learning is a subset of AI dedicated to developing techniques for making machines learn to solve these and other "human" problems without the insanely complex task of explicitly programming them.

A machine is said to learn if, with increasing experience, it gets better at solving a problem. Let's take identifying malware as an example. This is known as a classification problem. Let's also call into existence a theoretical machine learning program called Mavis. Consistent malware classification is difficult for Mavis because it is deliberately evasive and subtle.

For it to successfully classify malware, we need to show Mavis a huge number of files that are known to be malicious. Once Mavis has digested several million examples, it should be an expert in what makes a file "smell" like malware.

The spectrum of ways in which Mavis might be programmed to learn this task is very wide indeed, and filled with head-spinning concepts and algorithms. Suitable approaches all have advantages and disadvantages. All that counts, however, it's whether Mavis can spot and stop previously unknown malware even when the "smell" is very faint or deliberately disguised to confuse it into an unfortunate misclassification.

A major problem for developers lies in proving that their implementation of Mavis intelligently detects unknown malware. How much training is enough? What happens when their Mavis encounters a completely new threat that smells clean? Do we need a second, signature-based system until we're 100% certain it's getting it right every time? Some vendors prefer a layered approach, while others go all in with their version of Mavis.

Every next generation security product vendor using machine learning says their approach is the best, which is entirely understandable. Like traditional AV products, however, the proof is in the testing. To gain trust in their AI-based products, vendors need to hand them over to independent labs for a thorough, painstaking work out. It's the best way for the public, private enterprises, and governments to be sure that Mavis in her many guises will protect them without faltering.


Friday, 18 November 2016

Recovering From Password Fatigue

How do we solve the need for lots of strong passwords?

Mention password strength online and someone will usually reference the famous XKCD password cartoon. If you haven't seen it, the idea is that the entropy of the password must be as high as possible, and that this can be adequately achieved by stapling together easily-remembered conjunctions of words
rather than difficult-to-remember strings of meaningless symbols. Some commentators have since pointed out flaws in the logic behind that cartoon.

Entropy is a head-twisting concept. Put simply, it is a measure of the chaos, disorder or unpredictability something contains. In information theory, entropy can be calculated and boils down to how many unknowns there are in a piece of data.

Consider a game of hangman. At the beginning of the game, none of the letters are known. Because there are many different possibilities, we can say that the unknown word contains high entropy. As you reveal each letter, the entropy quickly drops because of the way the English language works. Q is usually followed by U, for example, and not P or S or J. After revealing surprisingly few letters, we can usually infer the full word and win the game.

Passwords need high entropy. There should be no relationship between letters, so that if one character becomes known, it does not compromise the rest. If someone shoulder surfs you and spots you typing something like "M4nch3st" and they know you're a Manchester City or United fan from glancing at your coffee mug, then your carefully placed capital and number substitutions are all for naught.

Many people still think that strong passwords are required to protect from brute force attacks, but this is largely false. When cybercriminals want passwords, they either take them by the million using attacks such as SQL injections, or have people hand them over in phishing attacks. Because of this, we need lots of passwords to compartmentalise our lives into discrete blocks. Compromise one account and the others stay secure. Re-use them across accounts, and one key fits many locks.

There are lots of strategies for generating and remembering high entropy passwords. One successful technique is as follows:

1: Take a long line from a favourite book, play, song, nursery rhyme, whatever.
2: Take the initial letters from the words in the line and put them together.
3: Change vowels into numbers and other symbols, capitalise others.

Et voila! A long, high entropy password you cannot forget. Here's an example based on an episode of a sitcom that came to mind just now quite by chance:

In the Fawlty Towers episode The Germans, the Major says something like: "I must have been keen on her; I took her to see India!"

The 13 initials in this phrase are: imhbkohithtsi

Changing some letters to symbols and capitalising others gives: !mHbK0H1ThTsI

The online password strength meters I tried claim this password is strong or even very strong. Someone would have to know you were keen on that episode of that sitcom, guess the exact line from it, and guess exactly how you'd mangled the initials to stand a chance of recovering the generated password.

Now do that for the dozens of sites you need to log into, even those sites you intend to use very little but for which you must still set up an account. Ideally, each password must be different and unrelated. It's just not practical, is it? In fact, that sinking feeling you're probably experiencing has a name: password fatigue.

We could just store all our passwords in our browsers and create a master password to protect them. But what if we want to log in from another laptop, tablet or phone? This problem has led to the rise of the password manager.

A good password manger needs to securely store all your passwords, and to sync across all your devices. It should automatically capture the passwords you enter as it goes, and should contain some nice-to-have features. For example, the option to generate random, very high entropy passwords would be good. Intelligent form filling would also be useful.

There are other potential advantages to password managers. Because they recognise the sites you visit, if you get taken in by a phishing email and click on a link to enter your password, the manager will not recognise it, and should fail to cough up the creds. If you've allowed the manager to generate random passwords that you never see, there's no danger of you overriding it either.

I'm not going to recommend a single password manager, but you should check them out sooner rather than later. Instead I will point you to a comparison chart for you to make your own decision.

There are pros and cons to using password managers, however. Some people, like our own Simon Edwards, have argued that caution is needed. Last year, for example, cloud-based password manager LastPass was hacked and user data spilled (including security questions and encrypted passwords). Malware has also targeted local password managers such as KeepPass that do not use a cloud service.

Because of these weaknesses and attacks, passwords and password managers may not be enough. A good password manager also needs to feature 2-factor authentication. Biometric authentication would be even better as this is substantially harder to subvert.