CVE-2019-8622

Score / Severity

8.8

High

CVE Descriptions

Multiple memory corruption issues were addressed with improved memory handling. This issue is fixed in iOS 12.3, macOS Mojave 10.14.5, tvOS 12.3, watchOS 5.2.1, Safari 12.1.1, iTunes for Windows 12.9.5, iCloud for Windows 7.12. Processing maliciously crafted web content may lead to arbitrary code execution.

CVE Informations

Related Weaknesses

CWE-ID	Weakness Name	Source
CWE-787	Out-of-bounds Write The product writes data past the end, or before the beginning, of the intended buffer.

Metrics

Metrics	Score	Severity	CVSS Vector	Source
V3.1	8.8	HIGH	CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H More informations Base: Exploitabilty Metrics The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component. Attack Vector This metric reflects the context by which vulnerability exploitation is possible. Network The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. Such a vulnerability is often termed “remotely exploitable” and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers). Attack Complexity This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability. Low Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success when attacking the vulnerable component. Privileges Required This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability. None The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack. User Interaction This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable component. Required Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited. For example, a successful exploit may only be possible during the installation of an application by a system administrator. Base: Scope Metrics The Scope metric captures whether a vulnerability in one vulnerable component impacts resources in components beyond its security scope. Scope Formally, a security authority is a mechanism (e.g., an application, an operating system, firmware, a sandbox environment) that defines and enforces access control in terms of how certain subjects/actors (e.g., human users, processes) can access certain restricted objects/resources (e.g., files, CPU, memory) in a controlled manner. All the subjects and objects under the jurisdiction of a single security authority are considered to be under one security scope. If a vulnerability in a vulnerable component can affect a component which is in a different security scope than the vulnerable component, a Scope change occurs. Intuitively, whenever the impact of a vulnerability breaches a security/trust boundary and impacts components outside the security scope in which vulnerable component resides, a Scope change occurs. Unchanged An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority. Base: Impact Metrics The Impact metrics capture the effects of a successfully exploited vulnerability on the component that suffers the worst outcome that is most directly and predictably associated with the attack. Analysts should constrain impacts to a reasonable, final outcome which they are confident an attacker is able to achieve. Confidentiality Impact This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability. High There is a total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server. Integrity Impact This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. High There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component. Availability Impact This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability. High There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable). Temporal Metrics The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence in the description of a vulnerability. Environmental Metrics These metrics enable the analyst to customize the CVSS score depending on the importance of the affected IT asset to a user’s organization, measured in terms of Confidentiality, Integrity, and Availability.	nvd@nist.gov
V2	6.8		AV:N/AC:M/Au:N/C:P/I:P/A:P	nvd@nist.gov

EPSS

EPSS is a scoring model that predicts the likelihood of a vulnerability being exploited.

EPSS Score

The EPSS model produces a probability score between 0 and 1 (0 and 100%). The higher the score, the greater the probability that a vulnerability will be exploited.

Date	EPSS V0	EPSS V1	EPSS V2 (> 2022-02-04)	EPSS V3 (> 2025-03-07)	EPSS V4 (> 2025-03-17)
2021-04-18	1.87%	–	–	–	–
2021-09-05	–	1.87%	–	–	–
2021-10-17	–	1.87%	–	–	–
2022-01-09	–	1.87%	–	–	–
2022-02-06	–	–	53.03%	–	–
2022-04-03	–	–	20.51%	–	–
2023-03-12	–	–	–	6.77%	–
2023-08-20	–	–	–	6.18%	–
2023-09-10	–	–	–	6.35%	–
2023-10-08	–	–	–	6.48%	–
2023-10-22	–	–	–	7.92%	–
2023-11-26	–	–	–	8.18%	–
2024-06-02	–	–	–	8.18%	–
2024-08-25	–	–	–	7.53%	–
2024-12-15	–	–	–	9.16%	–
2024-12-29	–	–	–	7.31%	–
2025-02-09	–	–	–	5.47%	–
2025-01-19	–	–	–	7.31%	–
2025-02-16	–	–	–	5.47%	–
2025-03-18	–	–	–	–	27.09%
2025-03-18	–	–	–	–	27.09,%

EPSS Percentile

The percentile is used to rank CVE according to their EPSS score. For example, a CVE in the 95th percentile according to its EPSS score is more likely to be exploited than 95% of other CVE. Thus, the percentile is used to compare the EPSS score of a CVE with that of other CVE.

EPSS First.org

Date	Percentile
2021-04-18	–
2021-09-05	76%
2021-10-17	77%
2022-01-09	48%
2022-02-06	98%
2022-04-03	96%
2023-03-12	93%
2023-08-20	93%
2023-09-10	93%
2023-10-08	93%
2023-10-22	94%
2023-11-26	94%
2024-06-02	94%
2024-08-25	94%
2024-12-15	95%
2024-12-29	94%
2025-02-09	93%
2025-01-19	94%
2025-02-16	93%
2025-03-18	96%
2025-03-18	96%

Exploit information

Exploit Database EDB-ID : 46888

Publication date : 2019-05-20 22h00 +00:00
Author : Google Security Research
EDB Verified : Yes

See also https://bugs.chromium.org/p/project-zero/issues/detail?id=1699 for a similar issue. The DFG JIT compiler attempts to determine whether a DFG IR operation could cause garbage collection (GC) during its execution [1]. With this, it is then possible for the compiler to determine whether there could be a GC between point A and point B in a function, which in turn can be used to omit write barriers (see e.g. https://v8.dev/blog/concurrent-marking#reducing-marking-pause for an explanation of write barriers) [2]. For example, in case an (incremental) GC cannot happen between an allocation of an object and a property store to it, the write barrier after the property store can be omitted (because in that case the newly allocated object could not already have been marked, so must be white). However, if the analysis is incorrect and a GC can happen in between, then the emitted code can cause use-after-free issues, e.g. if an unmarked (white) object is assigned as property to an object that was marked during an unexpected GC (and is thus black). Since commit 9725889d5172a204aa1120e06be9eab117357f4b [3] "Add code to validate expected GC activity modelled by doesGC() against what the runtime encounters", JSC, in debug builds, asserts that the information computed by doesGC is correct: "In DFG::SpeculativeJIT::compile() and FTL::LowerDFGToB3::compileNode(), before emitting code / B3IR for each DFG node, we emit a write to set Heap::m_expectDoesGC to the value returned by doesGC() for that node. In the runtime (i.e. in allocateCell() and functions that can resolve a rope), we assert that Heap::m_expectDoesGC is true.". The following sample (found through fuzzing and then simplified), triggers such an assertion: function f(a) { return 0 in a; } for (let i = 0; i < 100000; i++) { const s = new String('asdf'); s[42] = 'x'; // Give it ArrayStorage f(s); } Here, the `in` operation is converted to a HasIndexedProperty DFG operation [4]. Since the String object has ArrayStorage (due to the additional element), DFGClobberize will report that it does not write to the heap [5]. Afterwards, doesGC reports that the operation cannot trigger GC [6]. However, during the execution of the operation (in the DFG JIT implemented by a call to operationHasIndexedPropertyByInt [7]) the code ends up in JSString::getIndex (to determine whether the index is valid in the underlying string), which can end up flattening a rope string, thus causing a heap allocation and thus potentially causing garbage collection. This is where, in debug builds, the assertion fails: ASSERTION FAILED: vm()->heap.expectDoesGC() ../../Source/JavaScriptCore/runtime/JSString.h(1023) : WTF::StringView JSC::JSString::unsafeView(JSC::ExecState *) const 1 0x10d67e769 WTFCrash 2 0x10bb6948b WTFCrashWithInfo(int, char const*, char const*, int) 3 0x10bba9e59 JSC::JSString::unsafeView(JSC::ExecState*) const 4 0x10bba9c6e JSC::JSString::getIndex(JSC::ExecState*, unsigned int) 5 0x10c712a24 JSC::JSString::getStringPropertySlot(JSC::ExecState*, unsigned int, JSC::PropertySlot&) 6 0x10d330b90 JSC::StringObject::getOwnPropertySlotByIndex(JSC::JSObject*, JSC::ExecState*, unsigned int, JSC::PropertySlot&) 7 0x10bbaa368 JSC::JSObject::getPropertySlot(JSC::ExecState*, unsigned int, JSC::PropertySlot&) 8 0x10d20d831 JSC::JSObject::hasPropertyGeneric(JSC::ExecState*, unsigned int, JSC::PropertySlot::InternalMethodType) const 9 0x10c70132f operationHasIndexedPropertyByInt