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Each January on the SEI Weblog, we current the ten most-visited posts of the earlier yr. This yr’s high 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: Learn how to 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 broaden, the query stays: will customers need the expertise and belief it? How can innovators design AI-enabled merchandise, companies, and capabilities which can be efficiently adopted, somewhat than discarded as a result of the system fails to satisfy operational necessities, equivalent to end-user confidence? AI’s promise is certain to perceptions of its trustworthiness.
To highlight a number of real-world situations, think about:
- How does a software program engineer gauge the trustworthiness of automated code technology instruments to co-write purposeful, high quality code?
- How does a physician gauge the trustworthiness of predictive healthcare functions to co-diagnose affected person situations?
- How does a warfighter gauge the trustworthiness of computer-vision enabled menace intelligence to co-detect adversaries?
What occurs when customers don’t belief these methods? AI’s means to efficiently accomplice with the software program engineer, physician, or warfighter in these circumstances will depend on whether or not these finish customers belief the AI system to accomplice successfully with them and ship the end result promised. To construct acceptable ranges of belief, expectations should be managed for what AI can realistically ship.
This weblog put up explores main analysis and classes realized to advance dialogue of tips on how to measure the trustworthiness of AI so warfighters and finish customers generally can understand the promised outcomes.
Learn the put up 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 may be nonetheless appreciable uncertainty in regards to the ZT transformation course of, nonetheless, in addition to how ZTA will in the end seem in follow. Current govt 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 govt 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 put up, which we tailored from a white paper, we element 5 ZT finest practices recognized throughout the two-day occasion, talk about why they’re vital, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.
Learn the put up 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 method a cease signal, as a substitute of stopping, the automobile quickens and goes by the cease signal as a result of it interprets the cease signal as a velocity restrict signal. How did this occur? Regardless that 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 velocity restrict signal. This straightforward act of placing stickers on a cease signal is one instance of an adversarial assault on ML methods.
On this SEI Weblog put up, I look at how ML methods might 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 through which an ML mannequin might be influenced and present how this taxonomy can be utilized to tell fashions which can be strong towards adversarial actions.
Learn the put up in its entirety.
#7 Play it Once more Sam! or How I Realized to Love Giant Language Fashions
by Jay Palat
“AI is not going to exchange 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). Current 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 jot down sentences and paragraphs of coherent content material. The priority surrounding these instruments is powerful – from New York colleges banning using 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 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 growth environments have had code technology and automation instruments for years, on this put up I’ll discover what new developments in AI and LLMs imply for software program growth.
Learn the put up in its entirety.
#6 Learn how to Use Docker and NS-3 to Create Sensible Community Simulations
by Alejandro Gomez
Typically, researchers and builders must simulate numerous forms of networks with software program that may in any other case be arduous to do with actual gadgets. For instance, some {hardware} might be arduous to get, costly to arrange, or past the talents of the crew to implement. When the underlying {hardware} shouldn’t be a priority however the important features that it does is, software program could be 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, nonetheless, it is not enough to simulate a community. A simulator may 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 many others. NS-3 permits such sorts of simulations by piping information from faucet interfaces (a function of digital community gadgets offered by the Linux kernel that cross ethernet frames to and from consumer area) into the working simulation.
This weblog put up presents a tutorial on how one can transmit dwell information 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 complicated setups and make repeatable simulations in seconds.
Learn the put up in its entirety.
#5 5 Challenges to Implementing DevSecOps and Learn how to Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the undertaking stage, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Lately, nonetheless, the dialogue has shifted to this system stage to align safety with enterprise aims. The perfect consequence of such a shift is one through which software program growth groups act in alignment with enterprise objectives, 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 growth 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 frequent challenges to implementing DevSecOps. This SEI Weblog put up articulates these challenges and supplies actions organizations can take to beat them.
Learn the put up in its entirety.
#4 Utility 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 growth, and, on the similar time, plunging everybody right into a world the place it’s arduous to differentiate the superbly shaped, but typically faux and incorrect, code generated by synthetic intelligence (AI) applications from verified and well-tested methods?
This weblog put up, which builds on concepts launched within the IEEE paper Utility 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 growth, the implications of incorporating LLMs into software-reliant methods, and the areas the place extra analysis and improvements are wanted to advance their use in software program engineering.
Learn the put up in its entirety.
#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 might be efficient in lots of conditions, Rust’s compiler may 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’s the potential for safety vulnerabilities–and malicious software program that may benefit from these vulnerabilities. On this put up, we’ll deal with customers of Rust applications, somewhat 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 essential for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on attainable 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 that may affect future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Heart (CERT/CC). This put up is the second in a sequence exploring the Rust programming language. The first put up explored safety points with Rust.
Learn the put up in its entirety.
#2 Software program Modeling: What to Mannequin and Why
by John McGregor and Sholom G. Cohen
Mannequin-based methods engineering (MBSE) environments are supposed to help 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 setting, help 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 varied stakeholders. The result’s an total discount of growth dangers.
When initially envisioned, the necessities for a product could appear to signify the suitable product for the stakeholders. Throughout growth, nonetheless, the as-designed product involves replicate 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 not represented and is not a legitimate 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 growth—are they nonetheless consultant of the carried out product present process integration?
One of many objectives for strong 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 parts at every growth step. The one authoritative supply will usually be a group of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a legitimate supply of full and proper verification and validation (V&V) actions. On this put up, we look at the questions above and different questions that come up throughout growth and use the solutions to explain modeling and evaluation actions.
Learn the put up in its entirety.
#1 Cybersecurity of Quantum Computing: A New Frontier
by Tom Scanlon
Analysis and growth of quantum computer systems continues to develop at a fast 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 clear up sure issues which can be classically intractable, which means a standard laptop can not full the calculations inside human-usable timescales. Given this computational energy, there’s rising dialogue surrounding the cyber threats quantum computer systems might pose sooner or later. As an 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 may be little or no dialogue, nonetheless, on how we’ll defend quantum computer systems sooner or later. If quantum computer systems are to develop into such invaluable property, it’s cheap to undertaking that they’ll finally 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 Expertise 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 considerations. This put up 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.
Learn the put up in its entirety.
Wanting Forward in 2024
We publish a brand new put up 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.
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