In the Linux kernel, the following vulnerability has been resolved:

sched/fair: Fix shift-out-of-bounds in load_balance()

Syzbot reported a handful of occurrences where an sd->nr_balance_failed can grow to much higher values than one would expect.

A successful load_balance() resets it to 0; a failed one increments it. Once it gets to sd->cache_nice_tries + 3, this should trigger an active balance, which will either set it to sd->cache_nice_tries+1 or reset it to 0. However, in case the to-be-active-balanced task is not allowed to run on env->dst_cpu, then the increment is done without any further modification.

This could then be repeated ad nauseam, and would explain the absurdly high values reported by syzbot (86, 149). VincentG noted there is value in letting sd->cache_nice_tries grow, so the shift itself should be fixed. That means preventing:

If the value of the right operand is negative or is greater than or equal to the width of the promoted left operand, the behavior is undefined.

Thus we need to cap the shift exponent to BITS_PER_TYPE(typeof(lefthand)) - 1.

I had a look around for other similar cases via coccinelle:

@expr@ position pos; expression E1; expression E2; @@ ( E1 » E2@pos | E1 » E2@pos )

@cst depends on expr@ position pos; expression expr.E1; constant cst; @@ ( E1 » cst@pos | E1 « cst@pos )

@script:python depends on !cst@ pos « expr.pos; exp « expr.E2; @@

Dirty hack to ignore constexpr

if exp.upper() != exp: coccilib.report.print_report(pos[0], Possible UB shift here)

The only other match in kernel/sched is rq_clock_thermal() which employs sched_thermal_decay_shift, and that exponent is already capped to 10, so that one is fine.

Weakness

The product reads data past the end, or before the beginning, of the intended buffer.

Affected Software

Potential Mitigations

• Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
• When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
• Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
• To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.

Related Attack Patterns

• https://cwe.mitre.org/data/definitions/540.html

References

• https://git.kernel.org/stable/c/2f3eab368e313dba35fc2f51ede778bf7b030b54
• https://git.kernel.org/stable/c/39a2a6eb5c9b66ea7c8055026303b3aa681b49a5
• https://git.kernel.org/stable/c/805cea93e66ca7deaaf6ad3b67224ce47c104c2f
• https://git.kernel.org/stable/c/80862cbf76c2646f709a57c4517aefe0b094c774