Subliminal images deceive Machine Learning video labeling

Current machine learning systems are not designed to defend against malevolent adversaries. Some teams succeeded already to fool image recognition or voice recognition. Three researchers from the University of Washington fooled the Google’s video tagging system. Google offers a Cloud Video Intelligence API that returns the most relevant tags of a video sequence. It is based on deep learning.

The idea of the researchers is simple. They insert the same image every 50 frames in the video. The API returns the tags related to the added image (with an over 90% high confidence) rather than the tags related to the forged video sequence. Thus, rather than returning tiger for the video (98% of the video time), the API returns Audi.

It has never been demonstrated that subliminal images are effective on people. This team demonstrated that subliminal images can be effective on Machine Learning. This attack has many interesting uses.

This short paper is interesting to read. Testing this attack on other APIs would be interesting.

Hossein, Hosseini, Baicen Xiao, and Radha Poovendran. “Deceiving Google’s Cloud Video Intelligence API Built for Summarizing Videos.” arXiv, March 31, 2017.


Ben Seri and Gregory Vishnepolsky from the society armis recently disclosed eight vulnerabilities present in various BlueTooth stacks. Their paper “The dangers of Bluetooth implementations: Unveiling zero day vulnerabilities and security flaws in modern Bluetooth stacks” thoroughly describes these vulnerabilities and derives some interesting lessons.

Some vulnerabilities may allow taking control of the Bluetooth device. These exploits do not need the target to be in discoverable mode. They just need to know the Bluetooth MAC address (BDADDR). Contrary to common belief, it is guessable even for non-discoverable devices. If the target generates Bluetooth traffic, then it BDAADR is visible in the access code. If it is not generating traffic, the widely accepted convention to use the same MAC address for Wifi than for Bluetooth may reveal it.

Once the attacker knows the BDADDR, he can use the exploits. One powerful vulnerability is due to some lack of implementation guidelines in the specifications for the “Just Works” authentication. For Android and Windows, if the attacker claims to be “No Input No output, No Man in the middle protection required and no bonding,” the target stealthily accepts the connection with limited security capabilities for a short period of time (due to the no bonding). Of course, any service that would require MiTM protection or bonding, and verifies the requirement, will refuse to operate over such connection. For Apple, the connection requests a validation by the user.

Once the attacker is linked to the unknowing target, it can try many attacks. My preferred ones are CVE-2017-0783 and CVE-2017-8628. They use a flaw in the specification of the Personal Area Network (PAN). This service has a low-level security requirement. This means that the previous attack grants access to the PAN without any authorization! The attacker can mount a pineapple attack over Bluetooth without the target being aware. In a Wifi Pineapple, the attacker impersonates an already known WIFI public network and can act as a man in the middle. In this case, the pineapple does not need to be a known network. Redoutable.

The PAN specification dated from 2003 and was never since revised. “Just works” and the newer authentication protocols were specified more recently. They changed the trust model and trust context. The older specifications were not analyzed to identify potential impacts.

The other vulnerabilities allow either buffer overflows or data leakage by exploring more than the attributed spaces.

The disclosure was part of a coordinated disclosure with Google, Microsoft, and Linux kernel team.

Conclusion: Verify that you installed the August and September security patches for your devices. They contain patches to these vulnerabilities.


French users seem aware of the risks and threats of illicit sites

The French HADOPI recently published an interesting paper “Etude sur les risques encourus sur les sites illicites,” i.e., a study on the risks incurred on illegal sites. They polled 1,021 Internet users older than 15. The first part of the study analyses the reported use of so-called illicit sites. The second part checks the awareness of these users of the associated risks.

The first part is very conventional and shows information that was already known for other markets. The results are neither surprising nor widely deviating from other countries. For instance, without surprise, the younger generations use more illicit sharing sites than the oldest ones.

Figure extracted from the report. In black, my proposed translation.

Music, movies and TV shows are the categories that are the most illicitly accessed.

The second part is more interesting than the first one. Most polled users claim to know the threats of Internet (scareware, spam, the slowdown of computer due to malware, adult advertisement, and change of browser’s settings) as well as the issues (theft of banking account, identity theft, scam, or ransomware). Nevertheless, the more using illicit content, the higher the risk of nuisance and prejudice. Not surprisingly, 60% of consumers who stopped using illicit content suffered at least on serious prejudice.

Figure extracted from the report. In black, my proposed translation.

Users seem to understand that the use of illicit content seriously increases the risks. Nevertheless, there is a distortion. The nuisance is more associated to illegal consumption than actual real prejudices.

Figure extracted from the report. In black, my proposed translation

The top four motivation of legal users is to be lawful (66%), fear of malware (51%), respect for the artists (50%) and a better product (43%). For regular illicit users, the top three motivation to use legal offer is a better quality (43%), fear of malware (42%) and being lawful (41%). 57% of illicit users claim that they intend to reduce or stop using illegal content. 39% of illicit users announce that they will not change their behavior. 4% of illicit users claim they plan to consume more illicit content.

We must always be cautious with the answers to a poll. Some people may not be ready to disclose their unlawful behavior. Therefore, the real values of illicit behavior are most probably higher than disclosed in the document. Polled people may also provide wrong answers. For instance, about 30% of the consumers is illicitly consuming software claim to use streaming! Caution should also apply to the classification between streaming and P2P. Many new tools, for instance, Popcorn time, use P2P but present a behavior similar to streaming.

Conclusion of the report

Risks are present on the Internet. Illicit users are more at risk than lawful users.

Users acknowledge that illicit consumption is riskier than legal consumption.

Legal offer is perceived as the safe choice.

Having been hit by a security problem pushes users towards the legal offer.

An interesting report, unfortunately, currently it is only available in French.



Law 9 – Quis custodiet ipsos custodes?

This post is the ninth post in a series of ten posts. The previous post explored the eighth law: If You Watch the Internet, the Internet Is Watching You. This Roman sentence from poet Juvenal can be translated as “Who will guard the guards themselves?” Every element of a system should be monitored. This also includes the monitoring functions. As often some parts of the security model rely on the detection of anomalies, it is key that this detection is efficient and faithful.

Any security process should always have one last phase that monitors the efficiency of the implemented practices. This phase creates the feedback loop that regulates any deficiency or inefficiency of the security process. The quality and probity of this last phase have a strong influence on the overall robustness of the security. For instance, the COBIT framework has one control point dedicated to this task: ME2 – monitor and evaluate internal control.

The beauty of Bitcoin’s model is that every user is the ward that surveys the other users. The Bitcoin system assumes that a majority of users will operate faithfully. The Proof Of Work is the consensus mechanism that enforces, in theory, this assumption. Mining is costly and managing the majority of the hashing power may be impossible for one actor. This assumption may be questionable with new cryptocurrencies that do not have a significant number of users and with the advent of mining pool.

Separate the roles; Divide and Conquer. The scope of controlling and managing roles should be kept as small as possible. Guards should have a limited scope of surveillance and restricted authority. This reduces the impact of a malicious insider or the influence of an attacker who hijacked an administrator or controller account. Where possible, the scope of roles should partly overlap or be redundant between several individuals. This trick increases the chances to detect an error or a mischief from an insider as success would require collusion.

For instance, reduce the scope of system administrators as they have the keys to the kingdom. Nobody should have all the keys of the kingdom. After the Snowden incident, NSA drastically reduced the number of its system administrators.

Read the logs; logfiles are an essential element for monitoring and auditing the effectiveness of the security. They will be useful to detect and understand security incidents. Nevertheless, their optimal efficiency is reached only when they are regularly analyzed to detect anomalies. Ideally, they have to be proactively analyzed. Applying only a-posteriori log analysis is a weak security stance. Logfiles are not to be used only for forensics purpose.

If you find this post interesting, you may also be interested in my second book “Ten Laws for Security.”  Chapter 10 explores in details this law. The book is available for instance at Springer or Amazon.


AlphaGo: Round three – The Supremacy

This will most probably my last post on AlphaGo. AlphaGo is the supreme go player.

As announced, end of May 2017, the “Future of Go Summit” occurred in China. During this event, AlphaGo Master won three games against Ke Jie, the top grand master. After this magisterial success, AlphaGo played its last competitive match. The Deepmind team will focus now on new challenges.