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Security '05 Paper
[Security '05 Technical Program]
Protecting Against Unexpected System Calls
C. M. Linn, M. Rajagopalan, S. Baker, C. Collberg, S. K. Debray, J. H. Hartman
Abstract:This paper proposes a comprehensive set of techniques which limit the scope of remote code injection attacks. These techniques prevent any injected code from making system calls and thus restrict the capabilities of an attacker. In defending against the traditional ways of harming a system these techniques significantly raise the bar for compromising the host system forcing the attack code to take extraordinary steps that may be impractical in the context of a remote code injection attack. There are two main aspects to our approach. The first is to embed semantic information into executables identifying the locations of legitimate system call instructions; system calls from other locations are treated as intrusions. The modifications we propose are transparent to user level processes that do not wish to use them (so that, for example, it is still possible to run unmodified third-party software), and add more security at minimal cost for those binaries that have the special information present. The second is to back this up using a variety of techniques, including a novel approach to encoding system call traps into the OS kernel, in order to deter mimicry attacks. Experiments indicate that our approach is effective against a wide variety of code injection attacks.
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Figure 2 is an illustration of the attack model. At the bottom is the system call handler inside the OS kernel. Executing this handler is the ultimate goal of the attacker. Rectangles represent functions and data structures; arrows represent information necessary to access them. For example, if the attacker knows the system call calling convention and proper system call number it can invoke the system call handler directly by synthesizing the proper code. Failing that, the attacker can invoke a function that performs the proper system call if it knows the function's address. Failing that, the attacker can get the functions's address from the symbol table or PLT/GOT if it knows the functions's name. And so on. Our methods of making the necessary information unavailable to the attacker are described in subsequent sections, as indicated by the labels on the arrows.
In essence, our goal is to distinguish system calls invoked illegally by attack code from those invoked legally as part of a program's normal execution. We begin with the simple observation that this objective can be achieved, in great part, by examining the address of the system call instruction: if this is not any of the known locations from which the application can possibly trap into the kernel, then it must be within attack code. This raises several issues, which we discuss in the remainder of this section and the next: (Section 3.1) how is the set of ``allowed'' system call instruction locations to be determined and associated with an executable? (Section 3.2) how should such information be used? (Section 3.3) how should dynamically linked libraries be handled? and, finally: (Section 4) what if the attack code co-opts an system call instruction that is part of the program code (or a dynamic library)?
We use post-link-time binary rewriting to identify the address
of each system call instruction in the
executable (our implementation currently uses the PLTO binary
rewriting system for Intel x86 ELF executables [31]).1This information is then added to the ELF executable as a new section, the
Interrupt Address Table (IAT); the associated headers in the
ELF file modified appropriately; and the file written back out.
The IAT section is an optional component of an ELF executable, allowing
executables that do not have this section to run as-is,
albeit without the protections we describe.
The information in the IAT consists of two values for
each system call instruction found in the binary:
the address of the instruction immediately following the
system call instruction; and 
the system call number associated
with it.
Notice that there is enough information in the IAT entries that the system call numbers passed into the kernel by the system call now become redundant: the kernel could use the address pushed on the stack by the system call instruction to obtain the corresponding system call number from the IAT. This turns out to be very useful, as discussed in Section 4, for disguising system call instructions and thwarting mimicry attacks.
We modified the Linux kernel to incorporate IAT information into the kernel data structure representing processes. When an executable is loaded, it is checked to see whether the executable file contains an IAT section. If it does, the contents of that section are copied into the kernel data structure for the corresponding process; otherwise, this field in the process structure is set to NULL. An executable that does not contain an IAT section is executed without any of the checks described in this paper; thus, third party software can be run as-is. The remainder of this discussion focuses exclusively on executables containing an IAT section.
When a system call instruction occurs during the execution of a process, the kernel checks that the address pushed on the stack by the `int 0x80' instruction appears in the IAT information for that process. A system call from an address found in the IAT is allowed to proceed; otherwise, a possible intrusion is signalled.
Unlike addresses in an executable binary, addresses in a dynamically linked library are not determined until the dynamic linker maps the library into the process's address space. This means that the dynamic linker must update a library's IAT after the library has been mapped, then make this updated IAT available to the kernel - the kernel cannot simply read the IAT from the library ELF file directly. The kernel then merges the information from the IAT into its internal data structure.
The dynamic linker uses a new system call to provide new IAT sections to the kernel. The arguments to this system call are the base address and size of the new IAT. If all libraries are mapped at program load time, the addresses of all system call instructions in the shared libraries will appear in the kernel level IAT for the process before it runs. The one exception to this is the interpreter (dynamic linker) itself, since it is a shared object and would not be able to make system calls before its own IAT section is loaded. This is not a problem because the kernel is responsible for mapping the interpreter into the executable (before the process begins execution), and it can therefore retrieve and patch the linker's IAT before the process begins to execute.
By default, the dynamic linker uses a lazy binding mechanism to map libraries - a library is not mapped until the process references it during execution. This makes the process vulnerable to mimicry attacks, and must be modified as discussed in Section 4.2.1.
The IAT information makes it possible to identify any system call made from the injected code, since the addresses for such instructions will not appear in the IAT. To get around this the attack code must use a system call instruction that is already in the program: either part of the program code, or in a shared library. This section discusses the forms such attacks take and the steps we take to prevent them.
To use a system call instruction that is part of the
program, the attack code must branch either to the system call
instruction itself, or to some location from which execution eventually
reaches a system call instruction, e.g., some function in the standard
C library. There are two possibilities. The first is that of a
``known address attack,'' in which the attack code jumps to a fixed
address that contains (or leads to) a system call instruction.
The second possibility represents a class of attacks we term scanning
attacks. Here, the attack code scans the application's code, starting from
a valid code address (e.g., using the return address on the runtime stack),
looking for a particular pattern of bytes; its aim is to identify
a code address from which execution can reach a system call
instruction. The pattern scanned for may be simply a byte sequence
for a particular instruction, e.g., the 2-byte sequence 0xcd80
encoding the system call instruction `int 0x80,' or
a longer sequence representing several instructions, e.g., some initial
prefix of the system() library function. Such attacks can take a
variety of forms, e.g.: set up the arguments to a particular system call,
then scan for, and jump to, an int 0x80 instruction; or set up
the arguments to a particular library routine (say, open()), then
scan for a byte signature for that routine and invoke it from the attack
code. The first possibility listed above, that of known address attacks,
can be foiled using a variety of techniques that make code addresses
unpredictable, e.g., address obfuscation [3]. The
remainder of this section therefore focuses on addressing scanning attacks.
There are two distinct components to our approach: disguising system
call instructions so that they are difficult to identify
(Section 4.1); and making it harder to use pattern
matching to identify specific functions (Section
4.2).
The IAT contains the addresses of legitimate system call instructions, making it easy for the kernel to decide whether or not a trap is a legitimate system call. The kernel checks the address of any instruction that causes a trap into the kernel against the IAT; a trap whose address is found in the IAT is processed as a system call, otherwise it is processed as a normal trap.
This scheme can be quite effective in disguising system call instructions. For example, since the Intel x86 architecture allows most arithmetic instructions to take a memory operand, an illegal address trap can be generated from a wide variety of innocuous-looking instructions, e.g., add, sub, mov, etc. Moreover, the particular instruction used to disguise a particular system call instruction in an application or library can be varied randomly across different systems.
From a practical perspective, disguising system call instructions in this manner makes it significantly harder for attack code to identify software trap instructions: instead of a handful of conspicuous `int 0x80' instructions, the attack code now has to contend with the possibility that pretty much any instruction in the program could potentially trap into the kernel. In even medium-sized programs, the number of such candidates could easily number in the hundreds of thousands. From a theoretical perspective, the problem of determining whether a given instruction--say, an add instruction with a memory operand--could cause a runtime exception is provably difficult: it is a straightforward reduction to flow-sensitive pointer aliasing, which is complete for deterministic exponential time [23].
We can imagine two classes of such attacks. An attack might
examine program metadata, e.g., symbol tables, to discover
information about functions; Section 4.2.1 discusses
ways to hinder such attacks. Alternatively, such an attack might scan the
program text itself, looking for specific byte sequences.
Given a function 
in a program 
, let 
be the shortest sequence
of instructions (or shortest byte sequence) that uniquely identifies
within . An attacker might examine his own copy of , offline, to
determine , then craft a scanning attack that searches for this
sequence. Sections 4.2.2, 4.2.3, and
4.2.4 discuss several different ways to thwart such
attacks.
It does not seem straightforward to address this problem while using lazy binding for dynamically linked routines, since the standard lazy binding mechanism relies on the availability of symbol information. Our solution, therefore, is to abandon lazy binding and opt for ``eager binding'' instead. The idea is to have the dynamic linker resolve all GOT entries during the initial setup operations it performs, after the dynamic libraries have been mapped into the process's address space, but before transferring control to the main program. We can do this for the standard linker (ld.so) simply by setting the LD_BIND_NOW environment variable. Once all the GOT entries have been resolved in this manner, there is no further need for the symbols and relocations for the shared libraries, and they may be discarded. While this can potentially increase the startup time for a process, we believe that its impact will be small.
Conceptually very similar to the idea of scanning a dynamically linked executable's symbol table is that of scanning a loaded shared object's symbol table. To address this problem, we add a little extra functionality to our wrapper linker (see Section 3.3). After linking is finished, either just before or directly after we discard the symbols in the executable, we unmap the memory regions in the shared libraries that contain symbol and relocation information. This makes them inaccessible to attack code; any attempt to scan these regions of the library results in a segmentation fault that can be caught and flagged as a potential intrusion.
A final problem is that the GOT (directly) and the PLT (indirectly) identify the entry points of all library routines needed by a dynamically linked executable. This can allow an attacker to obtain a function's entry point by exploiting knowledge of the structure of a process's GOT. For example, if a program uses only a few dynamically linked library routines, the number of GOT entries will be correspondingly small. In such cases, an attack may be able to guess the correct entry point for a desired function, with high probability, simply by randomly choosing an entry in the GOT. A simple protective measure to address this is to introduce many fake entries into the GOT and PLT. Because the GOT and PLT usually account for only a very small fraction of the size of an executable, the space impact of such fake entries will usually be small. A second problem is that the GOT may, by default, have a predictable layout, i.e., the same function may lie in the same GOT slot in many or all copies of the executable. This would allow an attacker to execute any of the GOT resident library functions without any guesswork. This can be handled by randomizing the order of the entries in both the PLT and GOT.
An alternative approach to handling the problems introduced by dynamic libraries is to abandon dynamic linking altogether in favor of static linking. The SLINKY project [7] has shown that with very minor effort (a small tweak to the operating system kernel and some additional system software) the overhead traditionally associated with static linking can be largely eliminated. The resulting statically linked and stripped binaries will contain no symbolic information exploitable by the adversary.
', `mov , ',
`push ; pop ', etc., where is any register; and
arithmetic computations into a register that is not live.
In each case, we have to ensure that none of the condition codes affected by
the inserted instructions is live at the point of insertion. It is worth
noting that some advanced viruses, e.g., encrypted and polymorphic viruses,
use a similar mechanism for disguising their decryption engines from
detection by virus scanners [33,40].
The approach can be enhanced using binary obfuscation techniques
[22].
The higher the frequency with which such instructions are inserted, the greater the disruption to the original byte sequence of the program, as well as the greater the runtime overhead incurred. One possibility to determining a ``good'' insertion interval would be to compare the byte sequences of all the functions (and libraries) in a program to determine, for each function, the shortest byte sequence needed to uniquely identify that function in that program, and thereby compute the length of the shortest byte sequence that uniquely identifies any function. Any insertion interval smaller than this length would be effective in disrupting such signature-based scanning attacks.
In principle, the attack code could overcome the effects of layout randomization by, in effect, disassembling the program and constructing its control flow graph, thereby essentially reverse engineering the program. While this is possible in principle if we assume no limits on the time and space utilization of the attack code, it would require the injected attack code to be dramatically larger, and more sophisticated, than attacks commonly encountered today. Moreover, such reverse engineering by the attack code can be thwarted using binary obfuscation techniques [22], which inject ``junk bytes'' into an executable to make disassembly algorithms produce incorrect results.
Another approach to thwarting scanning attacks is to divide the address space of the executable into non-contiguous segments, separated by ``pockets'' of invalid addresses. If the attack code accesses one of the invalid address pockets, it generates a trap into the kernel that can be recognized as an intrusion. On modern virtual memory systems, where memory protection is typically enforced at the level of pages, such pockets must appear at page boundaries and occupy an integral number of pages.
There are two distinct approaches creating such discontinuity. First, we can separate the code section into many segments, assigning to each successive segment a load address which leaves a gap from the previous segment's ending address. Second, we can create several gaps (via code insertion) in the executable sections and unmap them at runtime. The first approach has the disadvantage that the program header table will contain the exact addresses where pockets begin and end, which may introduce a vulnerability if the attacker happens to find the location of the program header table. The advantage of this scheme, however, is that the physical size of the executable on disk will experience only a minimal increase. The second approach has the disadvantage that the physical size on disk can increase dramatically. However, it offers the advantage that a careful implementation can actually hide the code that does the unmapping within the pockets themselves, preventing an attacker from discovering the location of the executable's pocket layout.
A straightforward approach to inserting pockets is to simply insert them at arbitrary page boundaries, adjusting in the obvious way any instruction that happens to span the page boundary, and inserting an unconditional jump to branch over the pocket. This approach is appealing because it introduces virtually no increase in memory requirements for the application. The unconditional branches, however, might act as an indicator of a valid continuation address that an attacker might follow to ``jump over'' pockets. An alternative approach, used in our implementation, is to insert pockets in locations where no modifications to control flow are necessary, namely, between functions. Since function boundaries are not guaranteed to lie on page boundaries, this approach requires adding some padding into the executable, which increases its memory footprint.
We integrated our ideas into plto [31], a general purpose binary rewriting tool for the Intel IA-32 executables. Our tool implements all of the ideas described in this paper, with the single exception of the handling of dynamically linked libraries (Section 3.3); plto currently handles only statically linked binaries. Our tool takes as input a statically linked relocatable binary, and outputs the executable that results from performing intraprocedural layout randomization, nop-equivalent insertions, pockets insertions, or system call obfuscation, in various combinations determined by command-line arguments.
Our experiments were run on an otherwise unloaded 3.2 GHz Pentium 4 processor with 1 GB RAM running Fedora Core 1. All kernel modifications necessary for this intrusion detection system were implemented in the Linux kernel, version 2.6.1. The changes required to the kernel were minimal, spanning only a handful of source files, including the file containing the trap handler entry code, the file containing the ELF specific loader module, and the files containing the main task structure definition and task structure handling routines.
One simple approach to evaluating the efficacy of our approach to detecting code injection attacks would be to subject it to several currently known viruses/worms. There are two major problems with such an approach. First, many different attacks may exploit the same kinds of underlying software vulnerabilities (e.g., a buffer overflow on the runtime stack), which means that the number of ``known attacks detected'' need not have any correlation with the variety of vulnerabilities that an IDS is effective against. Second, such an approach would completely ignore attacks that are possible in principle but which have not (yet) been identified in the wild. For these reasons, we opted against relying on known attacks to evaluate the efficacy of our approach. We decided, instead, to use a set of carefully constructed synthetic attacks, whose design we describe here.
We begin by observing that our work assumes that the attack code has been successfully injected into the system and then executed, and aims to prevent this executing attack code from executing a system call. The nature of the exploit by which the attack code was injected and executed--be it via a buffer overflow on the stack, an integer overflow, a format string vulnerability, or some other mechanism--is therefore unimportant: we can pick any convenient means to introduce ``attack code'' into a running application and execute this code. Furthermore, the particular application used for the attack is also unimportant, as long as it is a realistic application, i.e., one that is of reasonable size and which contains some appropriate set of system calls which we wish to protect. Accordingly, our efficacy experiments use a single vulnerable application, and a single code injection method, to introduce and execute attack code; this attack code varies from experiment to experiment and attempts to use a variety of different approaches to executing system calls. By using a carefully crafted collection of attacks in this manner, including both direct invocation of system calls using an `int 0x80' instruction in the attack code, and mimicry attacks involving scanning, we can gauge the efficacy of our approach to a wide variety of attacks.
We used the m88ksim program (from the SPEC-95 benchmark suite), a simulator for the Motorola 88000 processor, as our attack target. The program is roughly 17,000 lines of C code, which maps to a little over 123,000 machine instructions over some 835 functions (compiled with gcc -O3 and statically linked). We chose this program because it makes use of several potentially dangerous library calls, including open and system (which eventually makes the system call execve). We simulated direct attacks, i.e., where the injected code contains the `int 0x80' system instruction for the system call it attempts to execute, by injecting the attack code onto the runtime stack and branching to it; the mimicry attacks, which involved various different ways to locate and branch to system calls in the library routines, were written in C and linked in as part of the program.
This section discusses the specific classes of attacks we tested, and the outcome in each case.
The first class of attacks we consider execute a system call instruction directly from the injected attack code. In practice, such an attack might result from injecting executable code onto the stack or the heap and then branching to this code (e.g. the Morris worm). Our test that represents this sort of attack uses a simulated buffer overflow, where instructions are first pushed onto the stack, then executed by jumping into the code on the stack. The instruction sequence so injected contains a system call instruction `int 0x80' to invoke the system call, preceded by some instructions to set up the arguments to this system call.
Our tests show that such attacks are completely prevented via the interrupt verification mechanism proposed in Section 3. Upon executing an interrupt from any location not found in the IAT, the operating system correctly declares the interrupt malicious and takes appropriate action.
Since each binary is randomized on a per-install basis, as described earlier, we assume that the attacker is unaware of the absolute address of any particular function or instruction in the binary. The Code Red virus and Blaster worm are examples of known-address attacks that are thwarted if addresses are randomized. Bhatkar at al. have demonstrated the efficacy of such randomization techniques against this class of attacks [3]. We therefore did not separately examine known-address attacks in our experiments.
We examined several scanning attacks that used pattern matching to try and discover the locations of valid system call entry points. Under the assumption that library code addresses have been randomized, e.g., via address obfuscation [3], such attacks must discover the locations of suitable system calls as follows:
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55 // push %ebp b8 00 00 00 00 // mov $0x0,%eax 89 e5 // mov %esp,%ebp 85 c0 // test %eax,%eax 57 // push %edi 53 // push %ebx 8b 7d 08 // mov 0x8(%ebp),%edi 74 ff // je 8076d26Note that there is nothing special about this particular byte sequence, other than that it happens to be one that is known to lead to an execve system call. We could have just as easily chosen another byte sequence corresponding to code for a suitable system call.
b8 0b 00 00 00 // movl 0x$b8, %eax cd 80 // int $x80
cd 80 // int $x80
There are two aspects to the cost of the underlying IAT mechanism: the incremental cost for an individual system call, and the impact on the overall performance of realistic applications. For these evaluations, our benchmark programs were compiled using gcc version 3.2.2, at optimization level -O3, with additional command-line flags to produce statically linked relocatable binaries. These binaries were then processed using our tool, described above. Execution times were measured using the time shell command. Each timing result was gathered by running the program 5 times, discarding the lowest and highest execution times so obtained, and averaging the remaining 3 run times.
To evaluate the effect of IAT checking on an individual system call,
we measured the time taken to execute a lightweight system call
(getpid) and two moderate-weight ones (open, read), with
and without IAT. In each case, we used the rdtsc instruction to
measure the system time taken to make each call 
times in a loop (we used
for getpid, 100,000 for open, and 300,000
for read), and divided the resulting time by to get the average
time for a single call. We repeated this 10 times for each system call,
removed the highest and lowest run times, and averaged the remaining eight
run times. Table 1 shows the results.
Not surprisingly, getpid experiences the largest percentage increase
from incorporating IAT checks in the kernel, but the actual increase is
quite small, about 0.25 
sec per call on average. The additional
runtime overhead for open and read are quite small: 1% for
open and 2.5% for read. The reason read experiences a
larger increase than open is that in the program we used,
it happened to appear later in the IAT, which--because of the naive linear
search currently used by our implementation--led to a larger search time.
To evaluate the effect of IAT checks on realistic benchmarks, we used ten benchmarks from the SPECint-2000 benchmark suite.2 The results are shown in Figure 3. It can be seen from this that the effect of adding IATs on realistic applications is quite small: on average disk file sizes increase about 0.11%, total memory size by 0.45%, and execution time by 1.7%. This is not surprising, since for unmodified executables, the kernel executes only a few instructions per system call to discover that a process has no associated IAT and proceed without any further attention to verification. The overhead associated with executing binaries with the protection system enabled is only slightly higher. One reason for only such a small increase in runtime is that system calls compose such a small fraction of the overall runtime in general due to the low frequency of their occurrence.
The effect of using various techniques for thwarting mimicry attacks on execution time is shown in Figure 4(a). Pocketing incurs an overhead of 2.8% on average. The reason for this small overhead is that the unmapped pages inserted are loaded into memory only once and are never executed, There is, however, one program, crafty, for which pocketing incurs a significant overhead, of around 13.5%. This turns out to arise, not from the system call verification mechanism, but due to a combination of increased page faults and a degradation in instruction cache performance.
NOP insertion incurs a runtime overhead of 5% on average, with two programs, crafty and gcc, incurring overheads of 8.5% and 9% respectively. This overhead comes directly from the increase in the number of instructions executed.
Layout randomization incurs a cost of 5.7% on average, with three programs experiencing significant increases in runtime: crafty (9.3%), gcc (14.7%), and vortex (10.2%). The cost increases here arise primarily from a degradation in instruction cache performance (see, e.g., Pettis and Hansen [25]). Our experiments indicate that unless layout randomization is done carefully, it can lead to a large increase in the number of TLB misses, resulting in a significant degradation in performance.
In comparison to other system call tracing based approaches such as
Janus[16], Ostia[16], systrace[26] and ASC[29], the runtime overheads incurred are seen
to be quite modest3.
The worst case micro-benchmark overheads (35%) are a
much smaller than other approaches
(Janus: 
, Ostia: 
, systrace: 
and
ASC: 
).
Similarly, the worst case benchmark overhead (15%) are also quite comparable
(Janus: 8%, Ostia: 25%, systrace: 30% and ASC: 3%).
We considered two different aspects of space: the memory footprint of a program, and the amount of disk space it occupies. For each program, the disk space was obtained simply from the size of the executable file; its memory footprint was measured by examining the program header table and adding up the sizes of each segment that is to be loaded into memory (i.e., the PT_LOAD flag is set); in the case of pocket insertion, we then subtracted out the space occupied by pockets. In general, the disk and memory footprints of a program will be different, for two reasons. The first is that not all sections in the disk image of a program are placed in memory (e.g., the IAT section is not), while not all sections in memory are represented explicitly in the disk image (e.g., the bss section). The second is that the pocketing transformation introduces unused pages into the executable that affect its disk size but not its memory size. The increase in memory footprint size for our benchmarks is shown in Figure 4(b), with the effects on disk size shown in Figure 4(c).
The increase increase in the memory requirements of a program due to the
introduction of the IAT is minimal in user space and only approximately 
bytes in kernel space, where is the number of system calls in the
program (the IAT has two 4-byte entries
per system call). Since is typically quite small in most
programs, the memory impact of the IAT is also small. The bulk of
the memory increases result from the secondary defenses, i.e., layout
randomization, nop-equivalent insertion, and pocket insertion.
On average, the overall memory cost is not large, ranging from about 9% for
pocket insertion to 12% for NOP insertion, to about 20% for
layout randomization. The largest increases are seen for layout
randomization, where several benchmarks incur memory footprint increases
of around 25% (e.g., gcc: 26.2%; mcf: 24.5%; vortex:
23.7%).
The increases in disk size are also reasonable for both layout randomization and NOP insertion, with overheads of 21.6% and 13.5% respectively. However, the space requirements for pocket insertion are much larger than the respective memory requirement (89.5% on average). This is due to the fact that while the actual insertion of pockets does not increase the memory footprint of the affected executable since these pockets are unmapped at runtime, the padding still takes up space in the file.4
Argument Hijacking Attacks.
An argument hijacking attack is
one in which the goal is to replace
the arguments to a legitimate system call with those of an attackers
choice. A simple defence would rely on extending code randomization to
methodically
hide system call parameters. Randomization techniques can be used to try and
make it difficult for attack code to identify parameters,
by making it appear as though all system calls have the same number
of parameters (system call homogenization) and by randomizing the order
of arguments (argument randomization) in a way that potentially allows each
call site to use its own permutation [28]. Incorporating such a
scheme would be straightforward from an implementation perspective and would
require trivial modifications to the IAT. Specifically, for each entry in
the table one would need to store two additional fields indicating correct
parameter locations and the exact argument order.
A more sophisticated defence would be through the realization of a system call monitor [29]. To do this, one would need to modify the IAT so that each entry contains additional information which encodes for each system call argument a set of acceptable values. Thus each entry in the IAT would correspond to a call-site specific system call policy which can, at runtime, be checked each time the system call occurs. Techniques described by Rajagopalan et al. can directly be applied for automatically deriving system call policies and realizing more sophisticated monitors [29].
Interpreted programs.
A related problem arises with
interpreters embedded in applications.
Programs executed on such interpreters are viewed as data to the underlying
system. This means that if a ``bad'' system call is executed in interpreted
code--either because the injected attack code is interpreted, or because
the attack code has modified part of an interpreted program--then this
system call will be seen by the underlying defences as coming from a
legitimate address, and will be allowed to execute. Such attacks are not
addressed by the techniques proposed here.
Templates.
Implementations of object oriented programming languages,
such as C++, keep function pointers associated with the virtual methods in a
class in a structure called a vtable, which is typically stored in static
memory.
Scanning the static memory region to
locate vtables can be used as the starting point for a scanning attack.
If a vtable is identified, the attack can potentially bypass protection
mechanisms such as pocketing. One way to defend against this is using
fine-grained pocketing, possibly by inserting pockets between basic blocks.
Stack inspection.
Another related attack is one in which the
adversary examines the stack
in search of addresses (such as function return addresses or function pointers
stored in local variables) which can serve as a starting points for scanning
attacks.
Randomizing the order of local variables within activation records may prevent
known address attacks, and encrypting pointers (as done by PointGuard [8]) can be used to prevent accesses to return addresses and function
pointers. Note that for any of these advanced scanning attacks, the attacker
must inject a significantly complex mechanism such as an interpreter
or a simulator into the running program. A related side effect would be that
programs under attack would show a drastic increase in their cycle count. This
anamolous behaviour would potentially trigger an alarm in any intrusion
detection system.
Dynamic Interfaces.
Distributed object technologies such as
Microsoft COM suffer from
the same vulnerabilities as object oriented languages, in that
they place structures containing function pointers in memory. In COM,
interfaces to objects are dynamically generated and passed in from outside
the application program. Even if the application program was obfuscated,
these external objects would likely not be obfuscated, and the interface
structures could easily be scanned and identified by an attacker.
Furthermore, COM identifies its objects and interfaces through Globally
Unique Identifiers (GUIDs), and this makes it easy for an attacker to
determine objects and derive information such as the type and the operations
supported.
One solution that we propose would be the introduction of a small shim layer between the application and the COM infrastructure. Binary rewriting techniques could be used in the application program to encrypt the GUIDs and permute the function pointers in the COM interface structures. The shim layer would decrypt and de-permute these data structures before sending them to the COM infrastructure. Systems such as COM are significantly more complex and hence securing them is non-trivial, and an area of future work.
The work that is closest to ours is that Rabek et al., who propose monitoring the origin of library calls for the Windows operating system to prevent misuses of critical functions [27]. Their particular approach suffers mostly due to the fact that intercepting attack code at this level is vulnerable to mimicry attacks that ``spoof'' the return address on the stack. The approach can also be bypassed by the scanning attacks described here. Also related is the work of Bernaschi et al., who propose modifications to the Linux operating system to regulate the usage of security-critical system calls [2]. System calls are intercepted at the kernel level and are validated based on rules stored in database. An example rule is validation of arguments known to be valid or safe. A drawback of this approach is that it requires manual encoding of access control rules for individual system calls and applications.
Du Varney et al. have proposed embedding semantic information into ELF binaries via an added section [9]. This work aims to simplify the task of post-processing executables for security purposes using binary rewriting tools. Because of this, the nature of the information embedded into binaries by Du Varney et al. is very different from ours.
Bhatkar et al. propose the use of address obfuscation to foil known-address attacks [3]. The idea is to randomize the base addresses of the stack, heap and code regions, and add gaps within stack frames and at the end of memory blocks requested by malloc. This technique is effective against known address attacks but is susceptible to the scanning attacks described in this paper.
There is a wide body of literature on defending against code injection attacks. Several researchers have proposed static program analysis to detect potential vulnerabilities such as buffer overflows [15,20,36]. When applied thoroughly, such schemes have the advantage of not letting an attacker even begin an attack. One disadvantage of such schemes is that they require that programs be recompiled using special compilers. This makes it difficult to apply them to third-party software, where the source code is unavailable and the conditions under which the binary was produced are not known.
ExecShield [34] prevents code injection via buffer overflow by making the process's heap and stack non-executable. This is difficult on the x86 architecture because it lacks separate ``read'' and ``execute'' page protection bits; ExecShield solves the problem by limiting the size of the code segment and putting the stack and heap beyond the end. Although this technique prevents code injection attacks, it does not prevent overwriting the return address with the address of a library, e.g. system. ExecShield performs address randomization to mitigate this type of attack, although this requires compiler support. ExecShield does not support programs that legitimately have executable content on the stack such as trampolines. These programs must have a flag set in the executable header indicating that ExecShield must not be invoked.
Other techniques, such as StackGuard [10] and FormatGuard [11], aim to prevent control transfers to the attack code. As in the previous case, such schemes require that programs be recompiled using special compilers, include files, and/or libraries, making them difficult to apply to third-party software. Moreover, they can be bypassed by well-crafted attacks (see, e.g., [4,30]). There has been some recent work on disrupting the actual execution of attack code by means of ``instruction set randomization'' [1,18], but current proposals for this have the drawback high execution overheads in the absence of specialized hardware support. Finally, Chew and Song have proposed techniques such as randomization of system call numbers [5]; a drawback of such approaches is its inflexibility in dealing with third-party software.
The idea of constructing semantic models of ``legitimate'' system call behaviors for a program in terms of sequences of system calls, and monitoring departures from such models, was proposed by Forrest et al. [13,17,38] and subsequently explored by a number of researchers (see, for example, [12,19,32,35]). A drawback to this approach is that it is vulnerable to specific mimicry attacks [37]. Several of these schemes use the return address pushed by a system call to identify its call site [12,32]. While this resembles our approach of identifying legitimate system calls based on the return address pushed by the software trap instruction, a significant difference between the two approaches is that our use of the IAT mechanism allows for other defenses against mimicry attacks, in partcular the system call cloaking scheme described in Section 4.1. Another difference is that schemes that rely on using training inputs to construct their semantic models of ``good'' executions have the drawback that it is difficult to ensure adequate code coverage, making for the possibility of false positives; by contrast, our approach is static, and so does not not suffer from runtime code coverage issues.
The use of NOP-insertion and code layout randomization to obfuscate code structure were proposed by Forrest et al. [14]; however, this work does not describe an implementation or provide experimental results. Other work along these lines is that of Wroblewski [39]. Many of these ideas can be traced to the Cohen's work on system diversification [6]. Additional techniques for binary obfuscation, to hamper static disassembly, are described by Linn and Debray [22].
Finally, several authors have proposed static analyses and/or type-based schemes to detect potential security vulnerabilities that could lead to the injection and activation of attack code [15,20,24]. Such schemes have the considerable merit of preventing the injection of attack code in the first place, which renders moot the issues addressed in this paper. A major drawback with such schemes is that they assume sufficient control of the code bases of all of the applications to be run on a system, so as to allow their analyses to be run on the source code. This is not always a realistic assumption in practice, since many applications are sold or distributed only as binaries.
Code injection attacks on software systems have become commonplace. Such attacks must eventually execute one or more system calls to cause damage outside of the compromised process. This paper describes a comprehensive approach for preventing the execution of such system calls. The core idea is twofold: first, use a table of addresses of ``allowed'' system call interrupt instructions to determine whether a given system call was executed from attack code; and second, use several different techniques to thwart mimicry attacks that attempt to get around this by identifying and executing system calls in the program code or in libraries. Our experiments indicate that the technique is effective and incurs only small runtime overheads. From a pragmatic perspective, it is also flexible: first, it is possible to run unmodified third-party software transparently, if desired, without any problems; and second, the additional information needed for our approach can be obtained using a binary rewriting approach on an executable, which means that it is not necessary to recompile the source code for an application using special compilers or libraries.
The work of Linn, Rajagopalan and Debray was supported in part by the National Science Foundation under grants EIA-0080123, CCR-0113633, and CNS-0410918. Discussions with R. Sekar and comments by the anonymous reviewers were very helpful in improving the contents of the paper.
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This paper was originally published in the
Proceedings of the 14th USENIX Security Symposium,
July 31–August 5, 2005, Baltimore, MD Last changed: 3 Aug. 2005 ch |
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