Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Response
Bibliographic record
Abstract
There is often a considerable delay between the discovery of a vulnerability and the issue of a patch. One way to mitigate this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but only if one is available. Since application configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within applications, which enables them to be mechanically inserted with minimal knowledge of the application and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We have designed and implemented Talos, a system that mechanically instruments SWRRs into a given application, and evaluate it on five popular Linux server applications. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1× more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".