Spotlight: Shining a Light on Pivot Attacks Using In-network Computing
Bibliographic record
Abstract
Pivoting remains an economical and practical penetration method as it allows a malevolent actor to obtain access to a private network through compromised devices. There are various tools both on the web and native to many operating systems, making pivoting simple to execute, even with limited system access. Preventing these attacks is traditionally performed with detection software running on end hosts or with perimeter devices, e.g., firewalls. However, not all end-host devices are under administrator control, and attackers can work around defences using SSH tunnels or obscuring their IP addresses. Rather than relying on middleboxes or end hosts, we leverage a programmable data plane for both their unique vantage point and traffic processing capabilities. Our system makes no assumptions about the underlying traffic and requires no cooperation from end hosts. We showcase Spotlight, a P4-based system that reliably intercepts pivoting attacks while raising only a small number of alarms. We develop a prototype system and demonstrate its effectiveness against various attacks on real-world traces.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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".