Each January on the SEI Weblog, we current the ten most-visited posts of the earlier 12 months. This 12 months’s high 10 highlights our work in quantum computing, software program modeling, massive language fashions, DevSecOps, and synthetic intelligence. The posts, which have been printed between January 1, 2023, and December 31, 2023, are offered beneath in reverse order based mostly on the variety of visits.
#10 Contextualizing Finish-Person Wants: How one can Measure the Trustworthiness of an AI System
by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner
As potential functions of synthetic intelligence (AI) proceed to increase, 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, moderately than discarded as a result of the system fails to satisfy operational necessities, corresponding 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 purposeful, high quality code?
- How does a health care provider gauge the trustworthiness of predictive healthcare functions 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 programs? AI’s skill to efficiently companion with the software program engineer, physician, or warfighter in these circumstances is dependent upon whether or not these finish customers belief the AI system to companion 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 publish explores main analysis and classes realized to advance dialogue of the right way to measure the trustworthiness of AI so warfighters and finish customers typically can notice the promised outcomes.
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#9 5 Greatest 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 apply. Latest government orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and plenty of personal sector organizations are following swimsuit. 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 publish, which we tailored from a white paper, we element 5 ZT finest 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.
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#8 The Problem of Adversarial Machine Studying
by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge
Think about driving to work in your self-driving automotive. As you strategy a cease signal, as a substitute of stopping, the automotive hurries up and goes by the cease signal as a result of it interprets the cease signal as a pace restrict signal. How did this occur? Though the automotive’s machine studying (ML) system was skilled to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automotive into pondering 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 programs.
On this SEI Weblog publish, I study how ML programs may be subverted and, on this context, clarify the idea of adversarial machine studying. I additionally study the motivations of adversaries and what researchers are doing to mitigate their assaults. Lastly, I introduce a primary taxonomy delineating the methods through which an ML mannequin may be influenced and present how this taxonomy can be utilized to tell fashions which are sturdy towards adversarial actions.
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#7 Play it Once more Sam! or How I Discovered to Love Massive 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 massive 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 massive our bodies of textual content, these fashions can predict the following phrase to jot down sentences and paragraphs of coherent content material. The priority surrounding these instruments is robust – from New York faculties banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many functions are strictly restricted to writing textual content, a number of functions discover the patterns to work on code, as properly. The hype surrounding these functions ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to concern, 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 publish I’ll discover what new developments in AI and LLMs imply for software program improvement.
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#6 How one can Use Docker and NS-3 to Create Reasonable Community Simulations
by Alejandro Gomez
Typically, researchers and builders have to simulate numerous forms of networks with software program that might in any other case be exhausting to do with actual gadgets. For instance, some {hardware} may be exhausting to get, costly to arrange, or past the abilities of the crew to implement. When the underlying {hardware} just isn’t a priority however the important capabilities that it does is, software program could be a viable various.
NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer time 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 is not ample to simulate a community. A simulator would possibly want to check how knowledge behaves in a simulated community (i.e., testing the integrity of Person Datagram Protocol (UDP) visitors in a Wi-Fi community, how 5G knowledge propagates throughout cell towers and consumer gadgets, and so forth. NS-3 permits such sorts of simulations by piping knowledge from faucet interfaces (a function of digital community gadgets offered by the Linux kernel that go ethernet frames to and from consumer area) into the operating simulation.
This weblog publish presents a tutorial on how one can transmit dwell knowledge by 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 advanced setups and make repeatable simulations in seconds.
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#5 5 Challenges to Implementing DevSecOps and How one can Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the challenge degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Just lately, nevertheless, the dialogue has shifted to this system degree to align safety with enterprise aims. The best final result of such a shift is one through 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 further concentrate on safety actions all through all phases of the software program improvement lifecycle (SDLC), will help organizations notice this ideally suited state. Nevertheless, the shift from project- to program-level pondering raises quite a few challenges. In our expertise, we’ve noticed 5 frequent challenges to implementing DevSecOps. This SEI Weblog publish articulates these challenges and gives actions organizations can take to beat them.
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#4 Utility of Massive 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 massive 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 exhausting to differentiate the superbly shaped, but typically pretend and incorrect, code generated by synthetic intelligence (AI) applications from verified and well-tested programs?
This weblog publish, which builds on concepts launched within the IEEE paper Utility of Massive 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 programs, 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 safety features of the Rust programming language may be efficient in lots of conditions, Rust’s compiler could be very explicit on what constitutes good software program design practices. Every time design assumptions disagree with real-world knowledge and assumptions, there’s the potential of safety vulnerabilities–and malicious software program that may make the most of these vulnerabilities. On this publish, we are going to concentrate on customers of Rust applications, moderately than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is offered or not. These instruments are necessary for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on doable instructions through 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 which may influence future safety responses, together with by way of the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Middle (CERT/CC). This publish is the second in a sequence exploring the Rust programming language. The first publish 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 programs engineering (MBSE) environments are meant to assist engineering actions of all stakeholders throughout the envisioning, growing, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE atmosphere, assist efforts such because the automation of standardized evaluation strategies by all stakeholders and the upkeep of a single authoritative supply of reality about product data. The mannequin faithfully represents the ultimate product in these attributes of curiosity to numerous stakeholders. The result’s an total discount of improvement dangers.
When initially envisioned, the necessities for a product could seem to characterize the correct 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 parts, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is now not represented and is now not a sound supply of take a look at instances. Many questions come up, corresponding to
- How do I consider the failure of a take a look at?
- How can I consider the completeness of a take a look at set?
- How do I observe 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 carried out product present process integration?
One of many targets for sturdy design is to have an up-to-date single authoritative supply of reality through which discipline-specific views of the system are created utilizing the identical mannequin components at every improvement step. The one authoritative supply will typically be a set 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 publish, we study 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 speedy tempo. The U.S. authorities alone spent greater than $800 million on quantum data science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they’ll be capable of resolve sure issues which are classically intractable, which means a standard laptop can’t full the calculations inside human-usable timescales. Given this computational energy, there’s rising dialogue surrounding the cyber threats quantum computer systems could pose sooner or later. As an illustration, 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 defend quantum computer systems sooner or later. If quantum computer systems are to change into such invaluable belongings, it’s cheap to challenge that they’ll finally be the goal of malicious exercise.
I used to be not too long ago 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 area 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 publish will discover points associated to creating the self-discipline of cyber safety of quantum computing and description six areas of future analysis within the area of quantum cybersecurity.
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Trying Forward in 2024
We publish a brand new publish 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.
