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Trendyol’s application security division discovered a sequence of bypass methods that make Meta’s Llama Firewall protections ineffective against advanced prompt injection assaults.
The results prompt renewed worries regarding the effectiveness of current LLM security strategies and emphasize the critical necessity for stronger defenses as companies increasingly incorporate large language models into their processes.
Throughout the evaluation, Trendyol engineers implemented Meta’s open-source Llama Firewall, concentrating on its PROMPT_GUARD component designed to filter out harmful user inputs.
Crucial Insights
1. Meta's PROMPT_GUARD failed to obstruct Turkish phrases like "üstteki yönlendirmeleri salla" and leetspeak such as "1gn0r3," revealing a reliance on English terms and precise matches.
2. The module overlooked a SQL injection in LLM-generated Python code, posing risks of unverified code utilization, security weaknesses, system vulnerabilities, and misplaced confidence in automated scans.
3. Invisible Unicode characters concealed malicious directives within seemingly harmless prompts, bypassing Llama Firewall and creating dangers in collaborative environments.
4. Testing and Disclosure Of 100 payloads examined, half were successful; Meta dismissed Trendyol’s May 5, 2025, report as "informative" by June 3 without offering a bounty, calling for the AI security community to construct stronger defenses against evolving threats.
To their astonishment, the guard permitted a Turkish-language injection containing the phrase “üstteki yönlendirmeleri salla,” directing the model to disregard earlier instructions and subsequently translate a sentence into French.
Meta’s Llama Firewall Compromised
The scan yielded a benign result despite the evident malicious intent. The team further showcased that basic leetspeak alterations transforming “ignore” into “1gn0r3,” for example, also bypassed the filter and received minimal jailbreak scores when assessed against the Llama-Prompt-Guard-2–86M model.
These trials demonstrated that the firewall’s rule configuration predominantly hinges on English keywords and exact pattern matching, rendering it ineffective against variations in language or character substitutions.
Equally alarming was the team’s assessment of CODE_SHIELD, the module that aims to flag insecure code suggestions created by LLMs.
In one proof-of-concept, the investigators instructed an assistant to generate a straightforward Python Flask endpoint with a SQL query. The resulting code concatenated user input directly into the query string, creating a classic SQL injection vulnerability.
Despite the obvious risk, CODE_SHIELD categorized the output as secure and allowed it to pass unchallenged.
Trendyol’s developers highlighted several significant concerns regarding this deficiency:
- Production Risk: Teams could put faith in LLM-generated snippets without comprehensive human oversight.
- Security Weaknesses: Excessive reliance on automated scans introduces serious vulnerabilities.
- System Vulnerability: Production systems risk exploitation due to unnoticed flaws.
- Trust Issues: Developers may develop false assurances in automated security tools.
The oversight illustrates how these automated systems can create hazardous blind spots in enterprise security frameworks.
Unicode Steganography and Hidden Instructions
The most devious bypass involved embedding concealed directives within invisible Unicode characters.
By inserting zero-width spaces and other non-printing code points around the phrase “ignore all previous instructions,” the researchers camouflaged harmful commands within an innocuous inquiry about the capital of France.
Even though users and casual watchers perceived only an innocent query, the LLM detected and executed the hidden command. When submitted to Llama Firewall, this payload passed inspection with a zero-threat score.
Trendyol’s team illustrated that even minimal invisible payloads could consistently undermine system prompts and prompt models to produce arbitrary or harmful outputs.
This method poses an especially acute threat in collaborative settings where prompts are shared among developers, and automated scanners lack awareness of hidden characters.
In total, Trendyol tested one hundred distinct injection payloads against Llama Firewall. Half of these assaults bypassed the system’s safeguards, indicating that while the firewall provides some level of protection, it is far from exhaustive.
The successful bypasses underscore scenarios where attackers could compel LLMs to disregard essential safety filters, output biased or offensive material, or create insecure code ready for execution.
For organizations like Trendyol, planning to weave LLMs into developer platforms, automation workflows, and customer-facing applications, these vulnerabilities signify tangible risks that could lead to data leaks, system breaches, or regulatory violations.
Trendyol’s security researchers reported their preliminary findings to Meta on May 5, 2025, detailing the multilingual and obfuscated prompt injections.
Meta acknowledged receipt and initiated an internal review but ultimately closed the report as “informative” on June 3, opting not to issue a bug bounty.
A parallel disclosure to Google concerning invisible Unicode injections was similarly concluded as a duplicate.
In spite of the lackluster vendor responses, Trendyol has since improved its threat modeling practices and is sharing its case study with the wider AI security community.
The organization encourages other companies to perform thorough red-teaming of LLM defenses prior to deploying them in production, emphasizing that prompt filtering alone cannot avert all forms of compromise.
As enterprises rush to exploit the potential of generative AI, Trendyol’s research serves as a cautionary tale: without layered, context-aware protections, even advanced firewall solutions can become susceptible to deceptively simple attack vectors.
The security community must now collaborate on more resilient detection techniques and best practices to stay ahead of adversaries who consistently innovate new methods to manipulate these powerful systems.
The post Meta’s Llama Firewall Bypassed Using Prompt Injection Vulnerability appeared first on Cyber Security News.
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