Paper detail

SFIP: Coarse-Grained Syscall-Flow-Integrity Protection in Modern Systems

Growing code bases of modern applications have led to a steady increase in the number of vulnerabilities. Control-Flow Integrity (CFI) is one promising mitigation that is more and more widely deployed and prevents numerous exploits. CFI focuses purely on one security domain. That is, transitions between user space and kernel space are not protected by CFI. Furthermore, if user space CFI is bypassed, the system and kernel interfaces remain unprotected, and an attacker can run arbitrary transitions. In this paper, we introduce the concept of syscall-flow-integrity protection (SFIP) that complements the concept of CFI with integrity for user-kernel transitions. Our proof-of-concept implementation relies on static analysis during compilation to automatically extract possible syscall transitions. An application can opt-in to SFIP by providing the extracted information to the kernel for runtime enforcement. The concept is built on three fully-automated pillars: First, a syscall state machine, representing possible transitions according to a syscall digraph model. Second, a syscall-origin mapping, which maps syscalls to the locations at which they can occur. Third, an efficient enforcement of syscall-flow integrity in a modified Linux kernel. In our evaluation, we show that SFIP can be applied to large scale applications with minimal slowdowns. In a micro- and a macrobenchmark, it only introduces an overhead of 13.1% and 1.8%, respectively. In terms of security, we discuss and demonstrate its effectiveness in preventing control-flow-hijacking attacks in real-world applications. Finally, to highlight the reduction in attack surface, we perform an analysis of the state machines and syscall-origin mappings of several real-world applications. On average, SFIP decreases the number of possible transitions by 38.6% compared to seccomp and 90.9% when no protection is applied.

preprint2022arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.