ICARuS: Intercode-CTF Auto-Randomization System
Bibliographic record
Abstract
Large Language Models (LLMs) have seen rapid advancements since 2020 and have shown impressive capabilities in the domain of software development. This has raised both curiosity and concerns surrounding the cybersecurity capabilities of LLMs. Recent work has sought to quantify these capabilities by adapting publicly available capture-the-flag (CTF) challenges into a benchmark. While CTF challenges are an attractive choice for benchmarking LLM cybersecurity capabilities due to their self-contained nature across a wide-range of cyber-related skills, there is a risk of test-set contamination where the LLMs have been trained on publicly-available solutions. To measure the extent that LLMs can adapt to variations in these types of problems, we present ICARuS, a randomization framework that generates randomized instances of tasks defined in a previously published benchmark (InterCode-CTF). In addition, we estimate the complexity of each Intercode-CTF task and show model performance degradation is inversely correlated with the number of steps required to solve the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".