The Illusion of Randomness: An Empirical Analysis of Address Space Layout Randomization Implementations
August 27, 2024 Β· Declared Dead Β· π Conference on Computer and Communications Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lorenzo Binosi, Gregorio Barzasi, Michele Carminati, Stefano Zanero, Mario Polino
arXiv ID
2408.15107
Category
cs.CR: Cryptography & Security
Citations
7
Venue
Conference on Computer and Communications Security
Last Checked
5 months ago
Abstract
Address Space Layout Randomization (ASLR) is a crucial defense mechanism employed by modern operating systems to mitigate exploitation by randomizing processes' memory layouts. However, the stark reality is that real-world implementations of ASLR are imperfect and subject to weaknesses that attackers can exploit. This work evaluates the effectiveness of ASLR on major desktop platforms, including Linux, MacOS, and Windows, by examining the variability in the placement of memory objects across various processes, threads, and system restarts. In particular, we collect samples of memory object locations, conduct statistical analyses to measure the randomness of these placements and examine the memory layout to find any patterns among objects that could decrease this randomness. The results show that while some systems, like Linux distributions, provide robust randomization, others, like Windows and MacOS, often fail to adequately randomize key areas like executable code and libraries. Moreover, we find a significant entropy reduction in the entropy of libraries after the Linux 5.18 version and identify correlation paths that an attacker could leverage to reduce exploitation complexity significantly. Ultimately, we rank the identified weaknesses based on severity and validate our entropy estimates with a proof-of-concept attack. In brief, this paper provides the first comprehensive evaluation of ASLR effectiveness across different operating systems and highlights opportunities for Operating System (OS) vendors to strengthen ASLR implementations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted