Efficient Memory Allocator for Restricting Use-After-Free Exploitations
Bibliographic record
Abstract
Attacks on heap memory, encompassing memory overflow, double and invalid free, use-after-free (UAF), and various heap-spraying techniques are ever-increasing. Existing secure memory allocators can be generally classified as complete UAF-mitigating allocators that focus on detecting and stopping UAF attacks, type-based allocators that limit type confusion, and entropy-based allocators that provide statistical defenses against virtually all of these attack vectors. In this thesis, I introduce two novel approaches, SEMalloc and S2Malloc, for type- and entropy-based allocation, respectively. Both allocators are designed to restrict, but not to fully eliminate, the attacker's ability, using allocation strategies. They can significantly increase the security level without introducing excessive overheads. \n \nSEMalloc proposes a new notion of thread-, context-, and flow-sensitive 'type', SemaType, to capture the semantics and prototype a SemaType-based allocator that aims for the best trade-off amongst the impossible trinity. In SEMalloc, only heap objects allocated from the same call site and via the same function call stack can possibly share a virtual memory address, which effectively stops type-confusion attacks and make UAF vulnerabilities harder to exploit. \n \nS2Malloc aims to enhance UAF-attempt detection without compromising other security guarantees or introducing significant overhead. We use three innovative constructs in secure allocator design: free block canaries (FBC) to detect UAF attempts, random in-block offset (RIO) to stop the attacker from accurately overwriting the victim object, and random bag layout (RBL) to impede attackers from estimating the block size based on its address. \n \nThis thesis demonstrates the importance of memory security and highlights the potential of more secure and efficient memory allocation by constraining attacker actions.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".