Security Vulnerabilities
In Trail of Bits fickling versions up to and including 0.1.11, the UnsafeImportsML analysis pass unconditionally calls AnalysisContext.shorten_code(node) on every import node it inspects, regardless of whether the import is flagged as unsafe. This call registers the shortened code representation in the shared AnalysisContext.reported_shortened_code set. When the MLAllowlist analysis pass subsequently runs, it calls the same shorten_code() method, receives already_reported=True for every import, and executes a continue statement that skips its allowlist check entirely. This renders MLAllowlist dead code for all imports — it never evaluates whether an import is in the ML allowlist or not. The MLAllowlist pass was designed to catch imports of modules outside the known-safe ML ecosystem (torch, numpy, transformers, etc.) that slip past the UnsafeImports denylist. With MLAllowlist inoperative, any standard library module not in the UNSAFE_IMPORTS denylist can be invoked via pickle deserialization while fickling's check_safety() returns LIKELY_SAFE. The fickling.load() API chains check_safety() into pickle.loads() as an explicit security gate, meaning a LIKELY_SAFE verdict causes the payload to be deserialized and executed. The root cause is shared mutable state between independently-correct analysis passes — UnsafeImportsML works as designed in isolation, MLAllowlist works as designed in isolation, but the shared reported_shortened_code set causes UnsafeImportsML to poison MLAllowlist's deduplication logic.
CVSS Score
8.8
EPSS Score
0.003
Published
2026-07-04
Trail of Bits fickling versions up to and including 0.1.10 do not include the Python standard library modules _posixsubprocess, site, and atexit in the UNSAFE_IMPORTS denylist (fickle.py). Because these modules are absent from the denylist, fickling's check_safety() function returns LIKELY_SAFE with zero findings for pickle payloads that invoke dangerous functions including _posixsubprocess.fork_exec (C-level process spawner capable of executing arbitrary binaries), site.execsitecustomize (executes arbitrary site customization code), and atexit._run_exitfuncs (triggers all registered exit handler callbacks). The fickling.load() API chains check_safety() into pickle.loads() as an explicit security gate; a LIKELY_SAFE verdict causes the payload to be deserialized and executed. This shares the same root cause as CVE-2026-22607 (cProfile), CVE-2025-67748 (pty), and CVE-2025-67747 (marshal/types). OvertlyBadEvals does not flag these modules because they are standard library imports. UnsafeImports does not flag them because they are not in the denylist. The UnusedVariables heuristic is defeated by the SETITEMS opcode pattern.
CVSS Score
8.8
EPSS Score
0.003
Published
2026-07-04
In multi-tenanted deployments, the application consent management mechanism fails to correctly isolate consent scopes between tenants. Consent granted by a user for a specific SaaS application within one tenant can be incorrectly applied to SaaS applications with the same name in other tenants, leading to unintended cross-tenant consent sharing.
This vulnerability may result in the exposure of user data across tenants, enabling SaaS applications in different tenants to access and modify information without explicit user authorization. This can lead to unauthorized data access and privacy violations. This vulnerability has no impact if the deployment does not support multi-tenancy.
CVSS Score
3.5
EPSS Score
0.002
Published
2026-07-04
In nltk/nltk versions 3.9.3 and earlier, five Stanford interface classes (StanfordPOSTagger, StanfordNERTagger, StanfordParser, StanfordDependencyParser, and StanfordNeuralDependencyParser) are vulnerable to untrusted JAR code execution. These classes accept user-controllable JAR paths and execute them via the `java()` function, which invokes `subprocess.Popen()` without integrity verification. This vulnerability is identical to CVE-2026-0848, which was fixed for StanfordSegmenter by adding SHA256 verification. However, the fix was not applied to these additional classes, leaving them susceptible to arbitrary code execution when loading untrusted JAR files.
CVSS Score
7.8
EPSS Score
0.002
Published
2026-07-04
Improper access control in Microsoft Edge for Android allows an unauthorized attacker to bypass a security feature over a network.
CVSS Score
6.5
EPSS Score
0.005
Published
2026-07-03
Improper neutralization of input during web page generation ('cross-site scripting') in Microsoft Edge (Chromium-based) allows an unauthorized attacker to perform spoofing over a network.
CVSS Score
5.4
EPSS Score
0.002
Published
2026-07-03
Insufficient ui warning of dangerous operations in Microsoft Edge (Chromium-based) allows an unauthorized attacker to perform spoofing over a network.
CVSS Score
4.3
EPSS Score
0.004
Published
2026-07-03
Absolute path traversal in Microsoft Edge for Android allows an unauthorized attacker to disclose information locally.
CVSS Score
6.2
EPSS Score
0.003
Published
2026-07-03
Relative path traversal in Microsoft Edge for Android allows an unauthorized attacker to disclose information locally.
CVSS Score
6.8
EPSS Score
0.003
Published
2026-07-03
Improper input validation in Microsoft Edge (Chromium-based) allows an unauthorized attacker to execute code over a network.
CVSS Score
7.5
EPSS Score
0.003
Published
2026-07-03