Each January on the SEI Weblog, we current the ten most-visited posts of the earlier 12 months. This 12 months’s prime 10 highlights our work in quantum computing, software program modeling, giant language fashions, DevSecOps, and synthetic intelligence. The posts, which had been revealed between January 1, 2023, and December 31, 2023, are introduced under in reverse order based mostly on the variety of visits.
#10 Contextualizing Finish-Consumer Wants: Find out how to Measure the Trustworthiness of an AI System
by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner
As potential purposes of synthetic intelligence (AI) proceed to broaden, the query stays: will customers need the expertise and belief it? How can innovators design AI-enabled merchandise, providers, and capabilities which are efficiently adopted, fairly than discarded as a result of the system fails to satisfy operational necessities, equivalent to end-user confidence? AI’s promise is sure to perceptions of its trustworthiness.
To highlight a number of real-world eventualities, contemplate:
- How does a software program engineer gauge the trustworthiness of automated code era instruments to co-write useful, high quality code?
- How does a health care provider gauge the trustworthiness of predictive healthcare purposes to co-diagnose affected person circumstances?
- How does a warfighter gauge the trustworthiness of computer-vision enabled risk intelligence to co-detect adversaries?
What occurs when customers don’t belief these techniques? AI’s capability to efficiently associate with the software program engineer, physician, or warfighter in these circumstances is determined by whether or not these finish customers belief the AI system to associate successfully with them and ship the end result promised. To construct acceptable ranges of belief, expectations have to be managed for what AI can realistically ship.
This weblog submit explores main analysis and classes realized to advance dialogue of how one can measure the trustworthiness of AI so warfighters and finish customers typically can understand the promised outcomes.
Learn the submit in its entirety.
#9 5 Finest Practices from Business for Implementing a Zero Belief Structure
by Matthew Nicolai, Nathaniel Richmond, and Timothy Morrow
Zero belief (ZT) structure (ZTA) has the potential to enhance an enterprise’s safety posture. There’s nonetheless appreciable uncertainty in regards to the ZT transformation course of, nevertheless, in addition to how ZTA will in the end seem in observe. Latest government orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and lots of non-public sector organizations are following go well with. In response to those government orders, researchers at the SEI’s CERT Division hosted Zero Belief Business Days in August 2022 to allow business stakeholders to share details about implementing ZT.
On this weblog submit, which we tailored from a white paper, we element 5 ZT greatest practices recognized in the course of the two-day occasion, focus on why they’re vital, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.
Learn the submit in its entirety.
#8 The Problem of Adversarial Machine Studying
by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge
Think about using to work in your self-driving automobile. As you strategy a cease signal, as a substitute of stopping, the automobile quickens and goes via the cease signal as a result of it interprets the cease signal as a pace restrict signal. How did this occur? Although the automobile’s machine studying (ML) system was educated to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automobile into considering it was a 45-mph pace restrict signal. This straightforward act of placing stickers on a cease signal is one instance of an adversarial assault on ML techniques.
On this SEI Weblog submit, I look at how ML techniques will be subverted and, on this context, clarify the idea of adversarial machine studying. I additionally look at the motivations of adversaries and what researchers are doing to mitigate their assaults. Lastly, I introduce a primary taxonomy delineating the methods by which an ML mannequin will be influenced and present how this taxonomy can be utilized to tell fashions which are sturdy towards adversarial actions.
Learn the submit in its entirety.
#7 Play it Once more Sam! or How I Discovered to Love Giant Language Fashions
by Jay Palat
“AI won’t change you. An individual utilizing AI will.”
-Santiago @svpino
In our work as advisors in software program and AI engineering, we are sometimes requested in regards to the efficacy of AI code assistant instruments like Copilot, GhostWriter, or Tabnine based mostly on giant language mannequin (LLM). Latest innovation within the constructing and curation of LLMs demonstrates highly effective instruments for the manipulation of textual content. By discovering patterns in giant our bodies of textual content, these fashions can predict the following phrase to put in writing sentences and paragraphs of coherent content material. The priority surrounding these instruments is powerful – from New York colleges banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many purposes are strictly restricted to writing textual content, a number of purposes discover the patterns to work on code, as nicely. The hype surrounding these purposes ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to worry, uncertainty, and doubt (“LLMs are going to take my job”). Within the Communications of the ACM, Matt Welsh goes as far as to declare we’ve reached “The Finish of Programming.” Whereas built-in improvement environments have had code era and automation instruments for years, on this submit I’ll discover what new developments in AI and LLMs imply for software program improvement.
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#6 Find out how to Use Docker and NS-3 to Create Practical Community Simulations
by Alejandro Gomez
Typically, researchers and builders must simulate numerous kinds of networks with software program that might in any other case be laborious to do with actual gadgets. For instance, some {hardware} will be laborious to get, costly to arrange, or past the abilities of the staff to implement. When the underlying {hardware} just isn’t a priority however the important features that it does is, software program is usually a viable different.
NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer season of Code, and others. It has a excessive diploma of functionality to simulate numerous sorts of networks and user-end gadgets, and its Python-to-C++ bindings make it accessible for a lot of builders.
In some instances, nevertheless, it isn’t adequate to simulate a community. A simulator would possibly want to check how information behaves in a simulated community (i.e., testing the integrity of Consumer Datagram Protocol (UDP) site visitors in a Wi-Fi community, how 5G information propagates throughout cell towers and consumer gadgets, and so on. NS-3 permits such sorts of simulations by piping information from faucet interfaces (a characteristic of digital community gadgets offered by the Linux kernel that move ethernet frames to and from consumer area) into the operating simulation.
This weblog submit presents a tutorial on how one can transmit reside information via an NS-3-simulated community with the added benefit of getting the data-producing/data-receiving nodes be Docker containers. Lastly, we use Docker Compose to automate complicated setups and make repeatable simulations in seconds.
Learn the submit in its entirety.
#5 5 Challenges to Implementing DevSecOps and Find out how to Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the mission degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Lately, nevertheless, the dialogue has shifted to this system degree to align safety with enterprise goals. The perfect end result of such a shift is one by which software program improvement groups act in alignment with enterprise targets, organizational danger, and answer architectures, and these groups perceive that safety practices are integral to enterprise success. DevSecOps, which builds on DevOps rules and locations extra deal with safety actions all through all phases of the software program improvement lifecycle (SDLC), may help organizations understand this ideally suited state. Nevertheless, the shift from project- to program-level considering raises quite a few challenges. In our expertise, we’ve noticed 5 widespread challenges to implementing DevSecOps. This SEI Weblog submit articulates these challenges and offers actions organizations can take to beat them.
Learn the submit in its entirety.
#4 Software of Giant Language Fashions (LLMs) in Software program Engineering: Overblown Hype or Disruptive Change?
by Ipek Ozkaya, Anita Carleton, John E. Robert, and Douglas Schmidt (Vanderbilt College)
Has the day lastly arrived when giant language fashions (LLMs) flip us all into higher software program engineers? Or are LLMs creating extra hype than performance for software program improvement, and, on the identical time, plunging everybody right into a world the place it’s laborious to differentiate the superbly fashioned, but generally pretend and incorrect, code generated by synthetic intelligence (AI) packages from verified and well-tested techniques?
This weblog submit, which builds on concepts launched within the IEEE paper Software of Giant Language Fashions to Software program Engineering Duties: Alternatives, Dangers, and Implications by Ipek Ozkaya, focuses on alternatives and cautions for LLMs in software program improvement, the implications of incorporating LLMs into software-reliant techniques, and the areas the place extra analysis and improvements are wanted to advance their use in software program engineering.
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#3 Rust Vulnerability Evaluation and Maturity Challenges
by Garret Wassermann and David Svoboda
Whereas the reminiscence security and security measures of the Rust programming language will be efficient in lots of conditions, Rust’s compiler could be very explicit on what constitutes good software program design practices. At any time when design assumptions disagree with real-world information and assumptions, there may be the opportunity of safety vulnerabilities–and malicious software program that may make the most of these vulnerabilities. On this submit, we are going to deal with customers of Rust packages, fairly than Rust builders. We’ll discover some instruments for understanding vulnerabilities whether or not the unique supply code is out there or not. These instruments are necessary for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on potential instructions by which instruments and automatic code evaluation can enhance. We additionally touch upon the maturity of the Rust software program ecosystem as an entire and the way that may affect future safety responses, together with by way of the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Middle (CERT/CC). This submit is the second in a sequence exploring the Rust programming language. The first submit explored safety points with Rust.
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#2 Software program Modeling: What to Mannequin and Why
by John McGregor and Sholom G. Cohen
Mannequin-based techniques engineering (MBSE) environments are meant to help engineering actions of all stakeholders throughout the envisioning, creating, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE surroundings, help efforts such because the automation of standardized evaluation methods by all stakeholders and the upkeep of a single authoritative supply of reality about product info. The mannequin faithfully represents the ultimate product in these attributes of curiosity to varied stakeholders. The result’s an general discount of improvement dangers.
When initially envisioned, the necessities for a product could seem to signify the appropriate product for the stakeholders. Throughout improvement, nevertheless, the as-designed product involves mirror an understanding of what’s actually wanted that’s superior to the unique set of necessities. When it’s time to combine elements, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is not represented and is not a sound supply of check instances. Many questions come up, equivalent to
- How do I consider the failure of a check?
- How can I consider the completeness of a check set?
- How do I monitor failures and the fixes utilized to them?
- How do I do know that fixes utilized don’t break one thing else?
Such is the case with necessities, and far the identical ought to be the case for a set of fashions created throughout improvement—are they nonetheless consultant of the applied product present process integration?
One of many targets for sturdy design is to have an up-to-date single authoritative supply of reality by which discipline-specific views of the system are created utilizing the identical mannequin parts at every improvement step. The only authoritative supply will typically be a group of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a sound supply of full and proper verification and validation (V&V) actions. On this submit, we look at the questions above and different questions that come up throughout improvement and use the solutions to explain modeling and evaluation actions.
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#1 Cybersecurity of Quantum Computing: A New Frontier
by Tom Scanlon
Analysis and improvement of quantum computer systems continues to develop at a fast tempo. The U.S. authorities alone spent greater than $800 million on quantum info science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they are going to be capable of resolve sure issues which are classically intractable, that means a traditional laptop can’t full the calculations inside human-usable timescales. Given this computational energy, there may be rising dialogue surrounding the cyber threats quantum computer systems might pose sooner or later. For example, Alejandro Mayorkas, secretary of the Division of Homeland Safety, has recognized the transition to post-quantum encryption as a precedence to make sure cyber resilience. There’s little or no dialogue, nevertheless, on how we are going to shield quantum computer systems sooner or later. If quantum computer systems are to turn out to be such invaluable property, it’s affordable to mission that they are going to ultimately be the goal of malicious exercise.
I used to be just lately invited to be a participant within the Workshop on Cybersecurity of Quantum Computing, co-sponsored by the Nationwide Science Basis (NSF) and the White Home Workplace of Science and Know-how Coverage, the place we examined the rising discipline of cybersecurity for quantum computing. Whereas quantum computer systems are nonetheless nascent in some ways, it’s by no means too early to handle looming cybersecurity issues. This submit will discover points associated to creating the self-discipline of cyber safety of quantum computing and description six areas of future analysis within the discipline of quantum cybersecurity.
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Wanting Forward in 2024
We publish a brand new submit on the SEI Weblog each Monday morning. Within the coming months, search for posts highlighting the SEI’s work in synthetic intelligence, cybersecurity, and edge computing.
