The Ethics of Tracing Hacker Attacks through the Machines of InnocentPersons
Bibliographic record
Abstract
Victims of hacker attacks are increasingly responding with a variety of “active defense” measures, including “invasive tracebacks” that are intended to identify the parties responsible for the attack by tracing its path back to its original source. The use of invasive tracebacks raise ethical issues because, in most cases, they involve trespassing upon the machines of innocent owners. Sophisticated hackers attempt to conceal their identities by routing their attacks through layers of innocent agent machines and networks that are compromised without the knowledge of the owners. The use of invasive traceback technologies in such cases, then, involves an act is presumptively problematic from an ethical standpoint: intentionally entering upon the property of an innocent person without her consent constitutes a prima facie trespass. I argue that there is no ethical principle that would justify the use of invasive tracebacks by private persons or entities (as opposed to governmental persons or entities). First, I argue that invasive tracebacks cannot be justified under the Defense Principle, which allows one person to use proportional force to defend herself or other innocent persons from attacks. Second, I argue that, in ordinary cases, the use of an invasive traceback impacting innocent persons cannot be justified under the Necessity Principle, which permits the infringement of an innocent person’s rights when necessary to achieve a significantly greater good. Since these are the only applicable principles, I conclude that, in the absence of special circumstances, it is not ethically permissible for private parties and entities to implement invasive traceback technologies.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".