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6 Most Dangerous New Attack Techniques in 2015
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RetiredUser
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RetiredUser,
User Rank: Ninja
4/23/2015 | 3:24:13 PM
Data Engineering
Of these, I think it is Technique #1 that interests me the most in terms of how lethal it can actually be.  When you look at Data Engineering (acquire->ingest->transform->store->retrieve) programmatically, there is this incredible opportunity to inject AI into the software that drives illegal data engineering, especially when the source is internal, such as a whistleblower or disgruntled worker, and someone who can help place malicious code onsite.  Then, with endpoints working together, cyber criminals can program data manipulation activities that are innocuous and ride everyday traffic, raise no alarms, and have as their key attribute patience.  Sensitive data leaks have the potential to become almost imperceptible with the addition of dummy endpoints (shadow companies, banks, etc) receiving information from both malicious sources and unsuspecting (depending on how well the insider is able to plant malicious code).  In short, data science holds the key to taking what used to be blatant and forensic-heavy incidents and now burying it into the white noise of everyday electronic traffic. 
RyanSepe
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RyanSepe,
User Rank: Ninja
4/23/2015 | 9:34:53 AM
IoT
I definitely agree with most of these, especially with the IoT. Devices are being made smart down to the nearest toaster. (Aside from the satire in that statement the big example I have in mind right now is the smart watch) These devices will only become more prevalent and for security to keep up will be a challenge.


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It was discovered that read_file() in apport/hookutils.py would follow symbolic links or open FIFOs. When this function is used by the openjdk-17 package apport hooks, it could expose private data to other local users.
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PUBLISHED: 2021-06-12
It was discovered that read_file() in apport/hookutils.py would follow symbolic links or open FIFOs. When this function is used by the xorg package apport hooks, it could expose private data to other local users.