Don't get hooked: How to protect yourself against phishing
Author
Joe Haigh
•
Senior Technical Solutions Specialist
Updated
September 19, 2023
|
Published
September 12, 2023
|
min read
Key Points
What is phishing?
Phishing is when attackers attempt to trick you into doing 'the wrong thing' - such as clicking a bad link that will download malware, or direct you to a dodgy website.
Once inside your systems, they'll try to steal your identity, exfiltrate any sensitive data like passwords or customer information, or even close you out of your systems unless you pay a ransom. Phishing attempts typically mimic reputable companies, colleagues, customers or suppliers in a fake message which contains a link to a phishing website that the threat actor controls. It’s one of the most common forms of cybercrime, with up to 3.4 billion malicious emails sent every day.
You can usually spot a phishing email a mile away if you stay vigilant, but some are more subtle than others...
An email from an unusual domain (eg. centralisedtoenpuretheberfectingtherob?!)
An email from a domain that doesn't match the company (such as person@microsoftonline.phishingdomain.org.uk - this is not Microsoft!)
An unexpected domain for the company – e.g. in the example above, the suffix is ".org.uk" where it should be “.com” for Microsoft
An email with unexpected files attached
The name in the email sign off doesn’t match the sender’s email address
The email contains spelling mistakes and incorrect grammar
The email is badly structured or poorly formatted
Hovering over a button or hyperlink shows an unexpected destination (e.g. rs20.rs6.net in an email purporting to be from Microsoft)
Branding irregularities such as poor text alignment or low-resolution logos
Urgency or mention of negative consequences if an action is not performed
Any emails asking for banking details or personal information (e.g. National Insurance/Social Security numbers)
Real life examples of phishing
Obvious phishing attempts
Now you know the common phishing themes, here are some real examples that should be easy to spot...
More sophisticated phishing attempts
“Scammers see AI tech as a gold mine for phishing schemes.”
How is AI changing phishing?
While these phishing attempts can be easy to spot once you know the signs, AI has moved the goalposts. Scammers see AI tech as a gold mine for phishing. ChatGPT alone understands about 20 languages, so cyber criminals can create more in-depth, grammatically correct emails in a variety of languages that are harder for you to spot and catch. And email is just the beginning.
Fortunately, there’s a silver lining. Just as AI technology is changing the game for phishing attacks, it is also changing the game for phishing defense. Machine learning is developing AI algorithms to identify real-time threats and to approach cyber security in a predictive manner, rather than analyzing events after they’ve already happened. It can look for and analyze message context and identify anomalies that signal phishing attacks.
Reinforce your defences with Intruder
Privacy and security providers like Intruder are fighting back. Keeping your software up to date and downloading the latest patches is a simple but essential step. Our scanners can detect if your software is out of date and vulnerable to attack, while penetration testing can investigate social engineering techniques to prevent hackers accessing data that enables smarter phishing attacks in the first place.
To see how we can help safeguard your digital assets from phishing and other vulnerabilities, why not try us for a free 14-day trial?
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Don't get hooked: How to protect yourself against phishing
Joe Haigh
September 12, 2023
What is phishing?
Phishing is when attackers attempt to trick you into doing 'the wrong thing' - such as clicking a bad link that will download malware, or direct you to a dodgy website.
Once inside your systems, they'll try to steal your identity, exfiltrate any sensitive data like passwords or customer information, or even close you out of your systems unless you pay a ransom. Phishing attempts typically mimic reputable companies, colleagues, customers or suppliers in a fake message which contains a link to a phishing website that the threat actor controls. It’s one of the most common forms of cybercrime, with up to 3.4 billion malicious emails sent every day.
You can usually spot a phishing email a mile away if you stay vigilant, but some are more subtle than others...
An email from an unusual domain (eg. centralisedtoenpuretheberfectingtherob?!)
An email from a domain that doesn't match the company (such as person@microsoftonline.phishingdomain.org.uk - this is not Microsoft!)
An unexpected domain for the company – e.g. in the example above, the suffix is ".org.uk" where it should be “.com” for Microsoft
An email with unexpected files attached
The name in the email sign off doesn’t match the sender’s email address
The email contains spelling mistakes and incorrect grammar
The email is badly structured or poorly formatted
Hovering over a button or hyperlink shows an unexpected destination (e.g. rs20.rs6.net in an email purporting to be from Microsoft)
Branding irregularities such as poor text alignment or low-resolution logos
Urgency or mention of negative consequences if an action is not performed
Any emails asking for banking details or personal information (e.g. National Insurance/Social Security numbers)
Real life examples of phishing
Obvious phishing attempts
Now you know the common phishing themes, here are some real examples that should be easy to spot...
More sophisticated phishing attempts
“Scammers see AI tech as a gold mine for phishing schemes.”
How is AI changing phishing?
While these phishing attempts can be easy to spot once you know the signs, AI has moved the goalposts. Scammers see AI tech as a gold mine for phishing. ChatGPT alone understands about 20 languages, so cyber criminals can create more in-depth, grammatically correct emails in a variety of languages that are harder for you to spot and catch. And email is just the beginning.
Fortunately, there’s a silver lining. Just as AI technology is changing the game for phishing attacks, it is also changing the game for phishing defense. Machine learning is developing AI algorithms to identify real-time threats and to approach cyber security in a predictive manner, rather than analyzing events after they’ve already happened. It can look for and analyze message context and identify anomalies that signal phishing attacks.
Reinforce your defences with Intruder
Privacy and security providers like Intruder are fighting back. Keeping your software up to date and downloading the latest patches is a simple but essential step. Our scanners can detect if your software is out of date and vulnerable to attack, while penetration testing can investigate social engineering techniques to prevent hackers accessing data that enables smarter phishing attacks in the first place.
To see how we can help safeguard your digital assets from phishing and other vulnerabilities, why not try us for a free 14-day trial?
Limited public information is available about the vulnerability.
Red teamers, security researchers, detection engineers, threat actors have to actively research type of vulnerability, location in vulnerable software and build an associated exploit.
Tenable release checks for 47.43% of the CVEs they cover in this window, and Greenbone release 32.96%.
Day of CVE publish
😊
Vulnerability information is publicly accessible.
Red teamers, security researchers, detection engineers and threat actors now have access to some of the information they were previously having to hunt themselves, speeding up potential exploit creation.
Tenable release checks for 17.12% of the CVEs they cover in this window, and Greenbone release 17.69%.
First week since CVE publish
😐
Vulnerability information has been publicly available for up to 1 week.
The likelihood that exploitation in the wild is going to be happening is steadily increasing.
Tenable release checks for 10.9% of the CVEs they cover in this window, and Greenbone release 20.69%.
Between 1 week and 1 month since CVE publish
🥺
Vulnerability information has been publicly available for up to 1 month, and some very clever people have had time to craft an exploit.
We’re starting to lose some of the benefit of rapid, automated vulnerability detection.
Tenable release checks for 9.58% of the CVEs they cover in this window, and Greenbone release 12.43%.
After 1 month since CVE publish
😨
Information has been publicly available for more than 31 days.
Any detection released a month after the details are publicly available is decreasing in value for me.
Tenable release checks for 14.97% of the CVEs they cover over a month after the CVE details have been published, and Greenbone release 16.23%.
With this information in mind, I wanted to check what is the delay for both Tenable and Greenbone to release a detection for their scanners. The following section will focus on vulnerabilities which:
Have CVSSv2 rating of 10
Are exploitable over the network
Require no user interaction
These are the ones where an attacker can point their exploit code at your vulnerable system and gain unauthorised access.
We’ve seen previously that Tenable have remote checks for 643 critical vulnerabilities, and OpenVAS have remote checks for 450 critical vulnerabilities. Tenable release remote checks for critical vulnerabilities within 1 month of the details being made public 58.4% of the time, but Greenbone release their checks within 1 month 76.8% of the time. So, even though OpenVAS has fewer checks for those critical vulnerabilities, you are more likely to get them within 1 month of the details being made public. Let’s break that down further.
In Figure 10 we can see the absolute number of remote checks released on a given day after a CVE for a critical vulnerability has been published. What you can immediately see is that both Tenable and OpenVAS release the majority of their checks on or before the CVE details are made public; Tenable have released checks for 247 CVEs, and OpenVAS have released checks for 144 CVEs. Then since 2010 Tenable have remote released checks for 147 critical CVEs and OpenVAS 79 critical CVEs on the same day as the vulnerability details were published. The number of vulnerabilities then drops off across the first week and drops further after 1 week, as we would hope for in an efficient time-to-release scenario.
Figure 10: Absolute numbers of critical CVEs with a remote check release date from the date a CVE is published
While raw numbers are good, Tenable have a larger number of checks available so it could be unfair to go on raw numbers alone. It’s potentially more important to understand the likelihood that OpenVAS or Tenable will release a check of a vulnerability on any given day after a CVE for a critical vulnerability is released. In Figure 11 we can see that Tenable release 61% their checks on or before the date that a CVE is published, and OpenVAS release a shade under 50% of their checks on or before the day that a CVE is published.
Figure 11: Percentage chance of delay for critical vulnerabilities
So, since 2010 Tenable has more frequently released their checks before or on the same day as the CVE details have been published for critical vulnerabilities. While Tenable is leading at this point, Greenbone’s community feed still gets a considerable percentage of their checks out on or before day 0.
I thought I’d go another step further and try and see if I could identify any trend in each organisations release delay, are they getting better year-on-year or are their releases getting later? In Figure 12 I’ve taken the mean delay for critical vulnerabilities per year and plotted them. The mean as a metric is particularly influenced by outliers in a data set, so I expected some wackiness and limited the mean to only checks released 180 days prior to a CVE being published and 31 days after a CVE being published. These seem to me like reasonable limits, as anything greater than 6 months prior to CVE details being released is potentially a quirk of the check details and anything after a 1-month delay is less important for us.
What can we take away from Figure 12?
We can see that between 2011 and 2014 Greenbone’s release delay was better than that of Tenable, by between 5 and 10 days.
In 2015 things reverse and for 3 years Tenable is considerably ahead of Greenbone by a matter of weeks.
But, then in 2019 things get much closer and Greenbone seem to be releasing on average about a day earlier than Tenable.
For both the trendline over an 11-year period is very close, with Tenable marginally beating Greenbone.
We have yet to have any data for 2021 for OpenVAS checks for critical show-stopper CVEs.
Figure 12: Release delay year-on-year (lower is better)
With the larger number of checks, and still being able to release a greater percentage of their remote checks for critical vulnerabilities Tenable could win this category. However, the delay time from 2019 and 2020 going to OpenVAS, and the trend lines being so close, I am going to declare this one a tie. It’s a tie.
The takeaway from this is that both vendors are getting their checks out the majority of the time either before the CVE details are published or on the day the details are published. This is overwhelmingly positive for both scanning solutions. Over time both also appear to be releasing remote checks for critical vulnerabilities more quickly.
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