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Hackers have started attacks on Cisco RV110, RV130, and RV215 routers

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Image: Cisco // Composition: ZDNet

Two days after Cisco patched a severe vulnerability in a popular brand of SOHO routers, and one day after the publication of proof-of-concept code, hackers have started scans and attacks exploiting the said security bug to take over unpatched devices.

The vulnerability, tracked as CVE-2019-1663, was of note when it came out on February 27 because it received a severity score from the Cisco team of 9.8 out of a maximum of 10.

It received such a high rating because the bug is trivial to exploit and does not require advanced coding skills and complicated attack routines; it bypasses authentication procedures altogether; and routers can be attacked remotely, over the internet, without attackers needing to be physically present on the same local network as the vulnerable device.

Affected models include the Cisco RV110, RV130, and RV215, all of which are WiFi routers deployed in small businesses and residential homes.

This means that the owners of these devices won’t likely be keeping an eye on Cisco security alerts, and most of these routers will remain unpatched –unlike in large corporate environments where IT personnel would have already deployed the Cisco fixes.

According to a scan by cyber-security firm Rapid7, there are over 12,000 of these devices readily available online, with the vast majority located in the US, Canada, India, Argentina, Poland, and Romania.

All of these devices are now under attack, according to cyber-security firm Bad Packets, which reported detecting scans on March 1.

The company detected hackers scanning for these types of routers using an exploit that was published a day earlier on the blog of Pen Test Partners, a UK-based cyber-security firm.

It was one of the Pen Test Partners’ researchers, together with two other Chinese security experts, who found this particular vulnerability last year.

In its blog post, Pen Test Partners blamed the root cause of CVE-2019-1663 on Cisco coders using an infamously insecure function of the C programming language -namely strcpy (string copy).

The company’s blog post included an explanation of how using this C programming function left the authentication mechanism of the Cisco RV110, RV130, and RV215 routers open to a buffer overflow that allowed attackers to flood the password field and attach malicious commands that got executed with admin rights during authentication procedures.

Attackers who read the blog post appear to be using the example provided in the Pen Test Partners article to take over vulnerable devices.

Any owner of these devices will need to apply updates as soon as possible. If they believe their router has already been compromised, reflashing the device firmware is recommended.

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Key Criteria for Evaluating a Distributed Denial of Service (DDoS) Solution

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Although ransomware is making all the headlines today, it’s not the only kind of attack that can intrude between you and your customers. Distributed denial of service (DDoS) attacks, in which a target website is overwhelmed with spurious traffic, have become increasingly common.

Websites and online applications have become critical to how businesses communicate with their customers and partners. If those websites and applications are not available, there is a dollars and cents cost for businesses, both directly in business that is lost and indirectly through loss of reputation. It doesn’t matter to the users of the website whether the attacker has a political point to make, wants to hurt their victim financially, or is motivated by ego—if the website is unavailable, users will not be happy. Recent DDoS attacks have utilized thousands of compromised computers and they can involve hundreds of gigabits per second of attack bandwidth. A DDoS protection platform must inspect all of the traffic destined for the protected site and discard or absorb all of the hostile traffic while allowing legitimate traffic to reach the site.

Often the attack simply aims vast amounts of network traffic at the operating system under the application. These “volumetric” attacks usually occur at network Layer 3 or 4 and originate from compromised computers called bots. Few companies have enough internet bandwidth to mitigate this much of an attack on-premises, so DDoS protection needs to be distributed to multiple data centers around the world to be effective against these massive attacks. The sheer scale of infrastructure required means that most DDoS platforms are multi-tenant cloud services.

Other attacks target the application itself, at Layer 7, with either a barrage of legitimate requests or with requests carefully crafted to exploit faults in the site. These Layer 7 attacks look superficially like real requests and require careful analysis to separate them from legitimate traffic.

Attackers do not stand still. As DDoS protection platforms learn to protect against one attack method, attackers will find a new method to take down a website. So DDoS protection vendors don’t stand still either. Using information gathered from observing all of their protected sites, vendors are able to develop new techniques to protect their clients.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:

Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.

GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.

Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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Cloud Data Security

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Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.

This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:

  • Dynamic versus static
  • Scalability
  • Evolvability

In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.

This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.

Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.

The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.

Figure 1. Difference in Cumulative Policy Changes

The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.

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GigaOm Radar for Data Loss Prevention

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Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.

With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.

This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.

Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.

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